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University of New Mexico UNM Digital Repository Linguistics ETDs Electronic Theses and Dissertations 5-1-2016 Illustrating the prototype structures of parts of speech: A multidimensional scaling analysis Phillip Rogers Follow this and additional works at: http://digitalrepository.unm.edu/ling_etds Recommended Citation Rogers, Phillip, "Illustrating the prototype structures of parts of speech: A multidimensional scaling analysis" (2016). Linguistics ETDs. Paper 28. This Thesis is brought to you for free and open access by the Electronic Theses and Dissertations at UNM Digital Repository. It has been accepted for inclusion in Linguistics ETDs by an authorized administrator of UNM Digital Repository. For more information, please contact [email protected]. PhillipGordonRogers Candidate Linguistics Department Thisthesisisapproved,anditisacceptableinqualityandformforpublication: ApprovedbytheThesisCommittee: WilliamCroft,Chairperson MelissaAxelrod IanMaddieson i ILLUSTRATINGTHEPROTOTYPESTRUCTURESOFPARTSOFSPEECH: AMULTIDIMENSIONALSCALINGANALYSIS by PHILLIPGORDONROGERS B.A.,HISTORY,OHIODOMINICANUNIVERSITY,2009 THESIS SubmittedinPartialFulfillmentofthe RequirementsfortheDegreeof MasterofArts Linguistics TheUniversityofNewMexico Albuquerque,NewMexico May2016 ii ACKNOWLEDGMENTS First,Iwanttothankmyadvisor,Dr.BillCroft,forintellectualinspiration, thoughtfulguidanceandencouragement,andremarkablepatience.Hisinfluence permeatesallaspectsofthisthesis. IamalsogratefulforthesupportofcommitteemembersDr.MelissaAxelrod andDr.IanMaddieson.Theyhaveneverhesitatedtooffertheirtimeandwisdom— evenonshortnotice—regardingthisthesisorotherresearch. AspecialthanksgoesouttoDr.JamesandMariaMondlochfortheirtimeand willingnesstosharetheirknowledgeofK’ichee’Mayan.Eveninthefaceofabstract andrelativelyunnaturalelicitationprompts,theyrespondedwithpatienceand valuableinsight. IwouldliketoacknowledgefriendsandcolleagueswithwhomIhave discussedthisresearchorrelatedtopics,includingDanielHieber,Matthew Menzenski,StefanodePascale,MitchellSances,LoganSutton,MichaelWoods,and TimZingler. Last,andmostimportantly,IwouldliketothankmywifeChrista,my children—Isaac,NoaRose,andMicah—andtherestofourfamilies.Timespenton thisthesiswastimeawayfromthem.Inparticular,Christahasbeenboththeheart andthecornerstoneofourhousehold,keepingmegroundedandourkidsinspired. iii ILLUSTRATINGTHEPROTOTYPESTRUCTURESOFPARTSOFSPEECH: AMULTIDIMENSIONALSCALINGANALYSIS by PhillipGordonRogers B.A.,History,OhioDominicanUniversity,2009 M.A.,Linguistics,UniversityofNewMexico,2016 ABSTRACT RadicalConstructionGrammar(Croft2001)proposesthatpartsofspeech canbeexplainedasprototypesthatemergefromtheuseofbroadsemanticclasses ofwords—objects,properties,andactions—inbasicpropositionalactfunctionsof discourse—reference,modification,andpredication.Thistheorypredictsthateach ofthesebroadsemanticclasseswillbetypologicallyunmarkedinitsprototypical propositionalactfunctionandrelativelymarkedinotherpropositionalactfunctions. Becausethistheoryspeakstosuchabroadandfundamentalorganizationof linguisticstructure,therichstructureoftheseprototypeshasnotbeenfully exploredinacomprehensivemanner.Gradienceisakeycharacteristicof prototypes(Rosch1978),anditisfoundforpartsofspeechinthecontinuumfrom objectconceptstopropertyconceptstoactionconcepts.Thisgradience—andthe semanticprimitivesthatmotivatethecontinuum—areexploredinmoredetail withineachofthesebroadsemanticclassesthroughadiscussionoftheliteratureon nounclasses,adjectiveclasses,andverbclasses. Equippedwithalistofconceptualtargetsthatarepredictedtorepresentthe rangeofprototypicalityforeachpartofspeech,thisthesissetsouttoillustratetheir iv prototypestructures.Forelevengenetically,geographically,andtypologically diverselanguages,lexemesareidentifiedtorepresenttheseconceptualtargets.The criteriaoftypologicalmarkednessisusedtoidentifyasymmetriesinhowthese lexemesareformallyencodedrelativetoeachotheracrossthethreepropositional actfunctions.Thesemarkednessasymmetriesarethencodedforandillustratedina multidimensionalscalinganalysis. Theinsightofthespatialmodelistwofold:First,itillustratesthetrue prototypestructuresofpartsofspeechinwhichmanyconceptsclusteratthe prototypesandafewendupontheperipheries.Evenmore,itrevealstherelative strengthsoftheprototypes,confirmingthehypothesisofaweakeradjective prototype.Second,thespatialmodelshedslightonthesemanticprimitivesthat motivatetheprototypestructures,bothinternallyandinrelationtoeachother. v TABLEOFCONTENTS LISTOFFIGURES...................................................................................................................viii LISTOFTABLES......................................................................................................................ix LISTOFABBREVIATIONS.......................................................................................................x 1. Introduction.......................................................................................................................1 2. Background........................................................................................................................3 2.1. Partsofspeechasprototypes...........................................................................................5 2.2. Prototypestructureandthesemanticcontinuumofpartsofspeech.................6 2.2.1. Objectsubclassification................................................................................................................8 2.2.2. Propertysubclassification.......................................................................................................10 2.2.3. Actionsubclassification............................................................................................................13 2.3. Typologicalmarkedness..................................................................................................15 2.3.1. Structuralcoding..........................................................................................................................17 2.3.2. Behavioralpotential...................................................................................................................18 2.3.3. Motivationsfortypologicalmarkedness...........................................................................21 2.4. Sourcesoftheprototypestructures............................................................................23 2.5. Cuttinguptheconceptualspace....................................................................................25 2.5.1. Semanticmaps..............................................................................................................................25 2.5.2. Multidimensionalscalinganalysis........................................................................................26 3. Dataandmethodology................................................................................................30 3.1. Selectionofthelanguagesample..................................................................................30 3.1.1. Geneticandgeographicdiversity.........................................................................................30 3.1.2. Diversityinpartsofspeechstrategies...............................................................................32 3.2. Datasources.........................................................................................................................33 3.3. Attestedpatternsofmarkedness..................................................................................36 3.3.1. Reference.........................................................................................................................................36 3.3.2. Modification...................................................................................................................................38 3.3.3. Predication......................................................................................................................................40 3.4. Codingthedata...................................................................................................................44 3.5. Dimensionality....................................................................................................................46 4. Methodologicalchallenges.........................................................................................48 vi 4.1. Cross-linguisticcomparison...........................................................................................48 4.2. Scope.......................................................................................................................................49 4.3. Theoreticalconstraints....................................................................................................49 4.3.1. Distributionalpotential.............................................................................................................49 4.3.2. Non-prototypicalinflectionalpotential.............................................................................49 4.4. Practicalconstraints.........................................................................................................51 4.4.1. Availabilityofdata.......................................................................................................................51 4.4.2. Generalizabilityanddetail.......................................................................................................51 4.4.3. Gradienceingrammaticality...................................................................................................52 4.4.4. Variationthatisnotcapturedinthestudyduetolistofconcepts.........................54 4.4.5. Precisioninmeaning..................................................................................................................55 4.5. Linguisticconsiderations................................................................................................56 4.5.1. Morphemecounting...................................................................................................................56 4.5.2. Choosingamonglexicalcandidates.....................................................................................57 4.5.3. Compounds.....................................................................................................................................57 4.5.4. Diachrony........................................................................................................................................58 4.5.5. Constructionalvariation...........................................................................................................60 4.5.6. Semanticshift................................................................................................................................62 4.5.7. Apparentarbitrariness..............................................................................................................66 5. Resultsanddiscussion................................................................................................68 5.1. Distributionoftheprototypes.......................................................................................71 5.2. Nounprototype...................................................................................................................75 5.3. Adjectiveprototype...........................................................................................................78 5.4. Verbprototype....................................................................................................................82 5.5. Interpretingthedimensions..........................................................................................86 5.6. Futureresearch..................................................................................................................88 5.7. Experience,conceptualstructure,andlinguisticform.........................................89 6. Conclusions.....................................................................................................................91 LISTOFAPPENDICES............................................................................................................93 REFERENCES........................................................................................................................117 vii LISTOFFIGURES Figure1.Conceptualspaceforpartsofspeech(Croft2001:92).........................................6 Figure2.Geographicdistributionofthelanguagesample....................................................30 Figure3.MDSanalysisofpartsofspeechwithpointsonly..................................................70 Figure4.MDSanalysisofpartsofspeechwithcuttinglinesonly.....................................71 Figure5.MDSanalysisofpartsofspeechwithconceptlabelsonly.................................72 Figure6.MDSanalysisofpartsofspeechwithpointsandprototypes...........................74 Figure7.Nounprototype.....................................................................................................................76 Figure8.Adjectiveprototype.............................................................................................................79 Figure9.Verbprototype......................................................................................................................83 Figure10.MDSanalysisofpartsofspeechforthree,six,nine,andelevenlanguages (clockwisefromtopleft)............................................................................................................89 viii LISTOFTABLES Table1.Semanticpropertiesofprototypicalpartsofspeech(Croft2001:87)............7 Table2.Objectconceptsubclassificationandselectedexemplars....................................10 Table3.Propertyconceptsubclassificationandselectedexemplars..............................13 Table4.Actionconceptsubclassificationandselectedexemplars....................................15 Table5.Prototypicalinflectionalcategoriesforeachpropositionalactfunction (Crofttoappear).............................................................................................................................20 Table6.Summarydataforthelanguagesusedinthisstudy(geneticclassification andpopulationnumbersarefromEthnologue)...............................................................31 Table7.Selectedinsightsintothenatureofpartsofspeechfromtheauthorsofthe referencematerials.......................................................................................................................33 Table8.TensedistinctionsofMaliVerbsaccordingtoVerbClass,transitivity,and stativity(basedondataanddescriptioninStebbins2011).......................................42 Table9.AspectualdistinctionsinCreekVerbs(fromdataanddescriptioninMartin 2011)...................................................................................................................................................44 Table10.Fitnessstatisticsinone,two,andthreedimensionsfortheMDSanalysisof partsofspeech................................................................................................................................68 Table11.Semanticpropertiesofprototypicalpartsofspeech(Croft2001:87)........81 Table12.Semanticpropertiesmotivatingtheprototypestructuresofpartsof speech.................................................................................................................................................87 ix LISTOFABBREVIATIONS 1 2 3 ACC ASSOC B CAUS CONTR COP DEM DENOM DUR FOC FUT III INF IPFV LOC M NFUT NPRS NPST OBJ PERS PL POSS PRED PRF PRS PST RDP SG V VENT firstperson secondperson thirdperson accusative associative nounclass“B”marker(Ingush) causative contrastive copula demonstrative denominalizer durative focus future nounclass“III”marker(Mali) infinitive imperfective locative masculine non-future non-present non-past object personal plural possessive predicatingparticle perfect present past reduplication singular nounclass“V”marker(Ingush) venitive x 1. Introduction RadicalConstructionGrammar(Croft2001)proposesthatpartsofspeech (noun,adjective,andverb)1canbeexplainedasprototypesthatemergefromthe useofbroadsemanticclassesofwords—objects,properties,andactions—inbasic propositionalactfunctionsofdiscourse—reference,modification,andpredication. Thistheorypredictsthateachofthesebroadsemanticclasseswillbetypologically unmarkedinitsprototypicalpropositionalactfunctionandrelativelymarkedin otherpropositionalactfunctions. Becausethistheoryaddressessuchabroadandfundamentalorganizationof linguisticstructure,therichstructureoftheseprototypeshasnotbeenfully exploredinacomprehensivemanner.Gradienceisakeycharacteristicof prototypes(Rosch1978),anditisfoundforpartsofspeechinaconceptual continuumfromobjectstopropertiestoactions.Thisgradiencewillbeexploredin moredetailwithineachofthesebroadsemanticclassesthroughadiscussionofthe literatureonnounclasses,adjectiveclasses,andverbclasses. 1Certainlabelingconventionsareusedthroughoutthispapertomakeclearthe distinctionbetweenuniversalcategoriesandlanguage-particularones(following Comrie1976;Bybee1985;Croft2001).Semanticnotionssuchaspropertiesor semelfactives,aswellaspropositionalactfunctionssuchaspredication,willbe writtenwithalowercasefirstletter.Thesearecross-linguisticconcepts.Languageparticularcategories,suchasAdjectivesorParticiples,arecapitalized.Labelsfor reoccurringmorphosyntacticstrategies,suchasrelativeclausesorpredicate nominals,arelowercase,eveniftherearelanguagesthatdonotusethatparticular strategy.Finally,thecategoriesNoun,Adjective,andVerbareoftencapitalized, followingthetheoreticalstanceofthispaperthattheyrepresentlanguageparticularcategories.However,theydoappearinlowercasewhentheyrefer—for sakeofconvenience—totheconceptual-functionalprototypes,evenforprevious researchwhichimplicitlyorexplicitlycharacterizesthemdifferently. 1 Equippedwithalistofconceptualtargetsthatarepredictedtorepresentthe rangeofprototypicalityforeachofthepartsofspeech,thisthesissetsoutto illustratetheseprototypestructures.Forelevengenetically,geographically,and typologicallydiverselanguages,lexemeswillbeidentifiedtorepresentthese conceptualtargets.Iwillusethecriteriaoftypologicalmarkednesstoidentify asymmetriesinhowtheselexemesareformallyencodedrelativetoeachother acrossthethreepropositionalactfunctions.Thesemarkednessasymmetrieswillbe codedforandillustratedinamultidimensionalscalinganalysis. Thegoalsofthismultidimensionalscalinganalysisaretwofold:First,the analysisshouldconfirmthecharacteristicsoftrueprototypestructures:many conceptsclusteredattheprototypesandafewconceptsinhabitingthe peripheries—particularlybetweentwoprototypes.Evenmore,therelative strengthsoftheprototypesshouldbeevidentintheanalysis.Ofparticularinterest istheadjectiveprototype,whichhasbeenclaimedtobeweakeroreven incomparabletothatofnounsandverbs.Second,themultidimensionalscaling analysiscanshedlightonthesemanticprimitivesthatmotivatetheprototype structures,asrelevantsemanticfactorswillcorrelatestronglywithparticular dimensionsinthespatialmodel. 2 2. Background Theexactnatureofpartsofspeechhasbeenacentraltopicoflinguistic inquiryanddebatesinceatleastantiquity.Longbeforenativelanguagesofthe PacificNorthwestwerepresentedasachallengetotheuniversaldistinction betweennounsandverbs,Platofamouslydisputedtherealityof‘tableness’and ‘cupness’withDiogenestheCynic(Stanley1687).Atstakeisafundamental organizationoflanguagestructure,andmanytermshavebeenusedforthis phenomenonwithgreaterorlessertheoreticalimplications:partsofspeech,lexical classes,wordclasses,grammaticalcategories,etc.InthispaperIwillmostoftenuse partsofspeechforthesakeofconsistency;whenanyoftheothertermsareused, theyaremeanttobesynonymous.Furthermore,partsofspeechcangenerallyrefer toallthewordclassesinalanguage,fromlarge,lexical,openclassestosmall, functional,closedclasses—andeverythinginbetween.Thisthesisfocusesonthe basicpartsofspeechcommonlyreferredtoasnouns,verbs,andadjectives.Adverbs areoftenincludedwiththesethree,andalthoughtheycanbeexplainedbythe theorytowhichthispaperascribes,theyareexcludedinthisstudyinorderto reasonablylimititsscope. Itisawidely-heldbeliefthatpartsofspeechshouldbeidentifiedina particularlanguageusingformalcriteria(Schachter&Shopen2007).Thesecriteria includeaword’ssyntacticdistribution,itssyntacticfunction,anditsabilitytotake varioussyntacticand/ormorphologicalinflections.Itfollowsfromthisthatpartsof speecharelanguage-specific,andtherearecountlessexamplesintheliteratureto illustratehowaparticularlexicalclassinonelanguagedoesnothaveexactlythe 3 samemembersasthepurportedlyequivalentlexicalclassinanotherlanguage.In fact,thevariouscriteriawithinalanguagedonotalwaysyieldthesame classifications,andthisleadstoimportanttheoreticalquestions(Schachter& Shopen2007:4):Aretheretrulydiscretedistinctionsamongpartsofspeech?When doesaclusterofgrammaticalcriteriaindicateadistinctionbetweenclasses,and whendoesitindicateadistinctionbetweensubclasses?Whichcriteriaaremost important?Mostanalysesofpartsofspeechinparticularlanguagesfailtoanswer thesequestionsexplicitly,resultinginsomeamountofarbitrariness.Itseemsthat formalcriteriaarenotsufficienttoexplainboththelanguage-particularaspectsand cross-linguisticsimilaritiesofpartsofspeech. Althoughthedelineationofpartsofspeechinalanguageisaccomplishedon formalgrounds,theterminologyappliedtoalexicalclassonceithasbeenidentified isoftenbasedonthesemanticnatureofitsmembers.Forexample,alexicalclass identifiedinaparticularlanguagewilllikelybelabeledasNounsifmostofits membersincludepeople,places,andthings.However,thismethodformatching language-particularwordclasseswithunifyingsemanticlabelsisnotalways straightforward.Inalanguagewithtwodistinctclassesofpropertywords,which onegetsthetraditionalAdjectivelabelandwhatlabelisusedfortheotherclass? Thesekindsofsemanticconsiderationsarenotenoughtovalidatecomparisonof AdjectivesinonelanguagetoAdjectivesinanother.Thefactisthatlanguagesmake differentdecisionsregardinghowmanylexicalclasses,wheretodrawtheline betweenthem,andbywhichformalmeans. 4 Themissingcomponentinourunderstandingofpartsofspeechthusfaris discoursefunction.HopperandThompson(1984)arguethatalllinguisticforms lack‘categoriality’untilitisforcedonthembyuseinaparticulardiscoursefunction. However—foreshadowingaprototypetheorythatinvokesbothdiscoursefunction andlexicalsemantics—theywritethat“mostformsbeginwithapropensityor predispositiontobecomeNounsorVerbs”(1984:747).Morerecentmodels(Croft 1991;Hengeveld1992)invokethepropositionalactfunctionsofreference, modification,andpredication,whicharebasedonSearle’s(1969)workonspeech acts.Thesepropositionalactfunctionsrepresentdistinctcommunicativegoals: referenceservestoestablishareferent,modificationtomodifyanexistingreferent, andpredicationtoreportonareferent’sstateofaffairs. 2.1. Partsofspeechasprototypes Bringingtogetherpreviousinsights,Croft(1991,2001)proposesthatparts ofspeechcanbeexplainedasprototypesthatemergefromtheuseofbroad semanticclassesofwords—objects,properties,andactions—inthebasic propositionalactfunctionsofdiscourse—reference,modification,andpredication. Thistheorypredictsthateachofthesebroadsemanticclasseswillbetypologically unmarkedinitsprototypicalpropositionalactfunction—objectsinreference, propertiesinmodification,andactionsinpredication—andrelativelymarkedin otherpropositionalactfunctions.Typologicalmarkednesswillbedefinedand discussedinmoredetailinsection2.3,anditplaysanimportantroleinthe methodologyofthispaper.Fornowitwillsufficetosaythatpatternsoftypological 5 markednessarebasedonformalcriteria,whichaswehaveseenisacritical componentofanytheoryofpartsofspeech. Moreover,theprototypetheoryofpartsofspeechalsoaccountsforboththe semanticnatureofpartsofspeechandtheroleofdiscoursefunction.Figure1 featuresaconceptualspace(thistoolisdiscussedinmoredetailinsection2.5.1)for partsofspeechthatillustrateshoweachbroadsemanticclasscanbeemployedin eachpropositionalactfunction.Thedarkersectionsaretheprototypes:eachbroad semanticclassisaprototypeoftheconstructionsofonepropositionalactfunction. Figure1.Conceptualspaceforpartsofspeech(Croft2001:92) REFERENCE MODIFICATION PREDICATION OBJECTS object reference object modification object predication identity predication PROPERTIES property reference property modification property predication location predication ACTIONS action reference action modification action predication 2.2. Prototypestructureandthesemanticcontinuumofpartsofspeech Croft’sconceptualspaceforpartsofspeechillustrateswelltheprototypes thatresultfromcombinationsofbroadsemanticclassandpropositionalactfunction. However,afundamentalcharacteristicofprototypestructureistheabsenceof discreteboundaries(Rosch1978).Instead,prototypeshaveagradientdimension alongwhichmembersofthecategorycanbeidentifiedasmoreorlessprototypical 6 tothecategory.Thisgradiencearisesfromtheintersectionofmanyindividual attributes,whichinturncanbeeitherdiscreteorgradient.Themoreprototypicala memberisofacategory,themoreattributesithasincommonwithothermembers ofthecategoryandthefewerattributesithasincommonwithmembersof contrastingcategories. Forpartsofspeech,thecontinuumfromobjectstopropertiestoactionsisa gradientdimension.Concerningtheconceptualspaceofpartsofspeech,Croftwrites that,“theverticaldimensioncouldbeasfinelydividedassinglewordconceptsif necessary”(Croft2001:94).ThiscontinuumhypothesiswasfirstdevelopedbyRoss (1972),andhasbeenrefinedbyseveralotherssince(e.g.,Comrie1975;Givón1979; Dixon1982;Croft1991,2001).Givón(1979)introducedtheideathattime-stability differentiatesnounsfromverbs,andthatadjectivesfallsomewhereinthemiddleof thiscontinuum.Croft(2001)splitsthisconceptintotransitorinessandstativity,and heincludestwoothers:relationalityandgradability.Ahypothesisofthispaperis thatthemultidimensionalscalinganalysiswillreflecttheinfluenceofthese semanticprimitives.Morespecifically,theyareexpectedtodifferentiateobject, property,andactionconceptclusters. Table1.Semanticpropertiesofprototypicalpartsofspeech(Croft2001:87) Objects Properties Actions Relationality nonrelational relational relational Stativity state state process Transitoriness permanent permanent transitory Gradability nongradable gradable nongradable ThesemanticprimitivesinTable1wereproposedtomotivatethe distinctionsamongthethreebroadsemanticclasses.Butwhatabouttheinternal 7 structureofeach?Itisclearfromtheliteraturethattheseclassesarenotinternally homogeneouseitherformallyorsemantically.Fortunately,muchhasbeenwritten abouttheformalandsemanticvariabilitywithinthesegroups,althoughmostly undertheterminologyofnoun,adjective,andverbclasses.Infact,different semanticfactorsseemtoberelevanttovariabilityandprototypicalitywithineachof theseclasses.Foreachoneinturn,Iwillbrieflyreviewthepertinentliteratureand usetheinsightsthereintobuildalistofconceptualtargetsforuseinthisstudy.The goalisalistofconceptualtargetsthatrepresentthefullrangeofprototypicalityon whicheversemanticdimensionsareexpectedtoberelevant.Ifthesemanticfactors discussedintheliteraturearetrulymotivatingprototypicalityforaparticularpart ofspeech,theprototypestructurewillbeborneoutinthespatialmodel. 2.2.1. Objectsubclassification Themostimportantdevelopmentforunderstandingobjectconcept prototypicalityhasbeentheextendedanimacyhierarchyfirstformulatedby Silverstein(1976;seealsoDixon1979;Comrie1989;Croft2003).Thishierarchy actuallyincludesthreesemanticdimensions:person,referentiality,andanimacy (Croft2003:130).Theroleoftheextendedanimacyhierarchycanbefoundinmany areasofmorphosyntax.Awell-knownexampleistheinflectionofnounsfornumber (Corbett2000).Ifalanguageinflectsanimatenounsfornumber,itwillalsoinflect fornumberallthecategoriesthatarehigheronhierarchy(e.g.,humannounsand pronouns),butnotnecessarilythoseloweronthehierarchy(e.g.,inanimates).Some languagesinflectmostorallpronouns/nounsfornumber,whileothersinflectonlya 8 verylimitedsubset—eitherway,thehierarchydetermineswhichcategoriesare includedinthatsubset.Ithasbeensuggestedthatthesesemanticfactorsinteract withotherfactorssuchasdefinitenessandtopicality(Comrie1989);thisisalmost certainlytrue,butitisstillunclearhowmuchoftheireffectonmorphosyntactic patternsisdirectandhowmuchisindirect. Theroleofpersonisfoundamongpronouns,wherefirstandsecondperson pronounsgenerallyoutrankthirdpersonpronouns.Forpurposesofscopeandin considerationofthemethodologicalchallengesthattheywouldpresent,Ichosenot toincludepronounsinthisstudy.Thereisevidencethatpronounsare “superprototypical”membersofthenounprototype,lessmarkedthaneventhe mostprototypicalcommonnouns(Croft1991:126-8).Forexample,Toqabaqita Pronounsmakeathree-waynumberdistinctionwhileNounscontrastonlysingular andplural(Lichtenberk2008:325).Similarly,referentialityisrelevantin distinguishingpronounsfrompropernounsfromcommonnouns.Likepronouns, propernounswereleftoutofthisstudy,leavingnovariationuponwhich referentialitymightact. Thisleavesanimacy,whichishypothesizedforthisstudytomotivate prototypicalityamongobjectconcepts.ThelistofobjectconceptsinTable2isbased onwell-knownnounclasses(seeforexampleLehmann&Moravcsik2000;Croft,to appear),anditstronglyreflectsadimensionofanimacy.Foreachclass,exemplars werechosenfortheirlikelihoodtobefoundinallormostofthelanguagesofthis study.Whereitwaspossibletoincludetwoexemplarsfromasingleclass, complementssuchasmale/female,higheranimate/loweranimate,large/small, 9 manmade/natural,etc.wereincludedtomaximizevariation.Thesameprinciples forchoosingexemplarswereappliedforthepropertyconceptandactionconcept subclassificationsdiscussedbelow. Table2.Objectconceptsubclassificationandselectedexemplars Class Humans Kinship Animals Plants/PlantProducts Artifacts BodyParts Places Material/Substance Exemplars WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE 2.2.2. Propertysubclassification Adjectives(andthepropertyconceptstheyprototypicallyencode)have receivedagreatdealofattentionovertheyearsfortheircontroversialpartof speechstatus.Somelinguistsassertthatadjectivesareuniversalandonparwith nounsandverbsasbasicpartsofspeech,whileothersciteevidencefromparticular languagesinwhichadjectivesappeartobeincomparableornon-existent.Whilethis studydoesfindevidenceforthevalidityofauniversaladjectiveprototype,this findingdoesnotnecessitatethateverylanguagehasasolitary,dedicated,andeasily 10 delineatedcategoryofAdjectives.Rather,itisclearthatlanguage-particular Adjectivecategoriescomeindifferentshapesandsizes,sotospeak. Whilethevariationinadjectiveclassesissubstantial,itisnotunconstrained. Inhisfamousarticle/chapter,“Wherehavealltheadjectivesgone?”,Dixon(1982) identifiedsubclassesofpropertyconceptsbasedontheirsemanticcharacteristics. Hedemonstratedwithcross-linguisticdatahowsomeofthesesubclassesaremore centraltothecross-linguisticnotionofadjective,showingupasAdjectivesevenin languageswithsmallAdjectivewordclasses;theseareage,dimension,value,and colorconcepts.Othersweremoreperipheral,encodedasAdjectivesonlyin languageswithlargeAdjectiveclasses.Furthermore,hefoundtendenciesamong theseperipheralsubclasses:physicalpropertyandspeedconceptsaremorelikely toshowupasVerbsinalanguage(ifnotAdjectives),whilehumanpropensitywere morelikelytobeNouns.Dixon’sfindingshavesincebeenconfirmedandhis subclassificationrefinedbysubsequentresearch. Therearetwostudiesofadjectivalpredicationthatwereparticularly importantforourunderstandingofpropertyconcepts.Inhistypologyofadjectival predication,Wetzer(1996)foundthateveninlanguageswhereAdjectivesare distinctfromNounsandVerbs,theyshowcharacteristicsthatmakethemeither “nouny”or“verby”.Heconceptualizedthesefindingsaslanguage-specifictreatment ofpropertyconceptsonthenoun-verbcontinuum,whereadjectivesaresomewhere inthemiddle.Interestingly,heconcludesthatadjectivesareverbybydefault. Inasimilarvein,LeonStassen’sIntransitivePredication(1997)examined formalpatternsofintransitivepredicationinalargelanguagesample.Semantically, 11 hedividedintransitivepredicationintofourtypes:event,class-membership, locational,andproperty-concept(representingverbs,nouns,adverbs,and adjectives,respectively).Notably,hefoundthatconceptpredicatesdonothavetheir ownprototypicalencodingstrategy,butinsteadtakeonthestrategyofoneofthe othertypes.Basedontimestability,heestablishedanadjectivehierarchysimilarto Dixon’s,fromhumanpropensityatoneendtogenderandmaterialattheotherend. Thiscontinuumcorrelateswiththefrequencyatwhichthoseconceptstakenounlikeversusverb-likeencodingstrategies.Furthermore,Stassenintroducesa tensednessparameterthatpitsmandatorytenseinflectiononVerbsagainstaverby treatmentofAdjectivesinpredication.Simplyput,alanguagewillnothaveboth,as thatwouldresultinmandatorilyassigningtensetorelativelytime-stableproperty conceptsforwhichtenseisnotrelevantorappropriate.Thetensednessparameter supportsthehypothesisthattemporalsemanticprimitivesareamongthose motivatingtheprototypestructuresofpartsofspeech. BothWetzerandStassenfocusonthemorphosyntacticrealizationof propertiesinpredication.Aswe’veseen,predicationisjustonepropositionalact functioninwhichpropertyconceptscanbeused,anditisn’teventheprototypical functionforpropertyconcepts.However,theirworkrefinedourunderstandingof thewhereparticularpropertyconceptsfallinthesemanticcontinuum.Furthermore, theirconsiderationofanimpressiverangeofcross-linguisticdataservedto elucidatethevariouswaysinwhichpropertyconceptscanbeformallyencoded relativetoobjectconceptsandactionconcepts.Whenweconsiderproperty 12 conceptsinpropositionalactfunctionsotherthanpredication,manyofthesame patternsemerge. Justasforobjectconcepts,eightsubclassesofpropertyconceptswere identifiedforuseinthisstudy.ThesearefoundinTable3,withtwoexemplars representingeachclass.Thesubclassesareorderedfrommostobject-likeatthetop tomostaction-likeatthebottom. Table3.Propertyconceptsubclassificationandselectedexemplars Class Gender Color Form Age Value Dimension PhysicalProperties HumanPropensity Exemplars MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY 2.2.3. Actionsubclassification Thesubclassificationofactionconcepts(‘verbclassification’,formany)has beendominatedbyadiscussionofaspectualstructure.Vendler(1967)identified fourverbclasses—states,activities,accomplishments,andachievements—and demonstratedforEnglishhowcertaingrammaticalbehaviorswerepredictable basedonaVerb’saspectualclassification.Thedistinctionsamongtheseclassescan 13 beattributedtosemanticprimitivessuchasduration,telicity,andstativity.For example,statesaredurative,atelic,andstative,whileachievementsare instantaneous,telic,anddynamic.Laterresearchconfirmedthevalidityofthese aspectualclassesforotherlanguages,andrefinedtheclassification(Comrie1976; Smith1997;Croft2012;seealsoChafe1970;Hopper&Thompson1980;Langacker 1987;interalia).Forexample,cyclicachievements(alsoknownassemelfactives) havebeenidentifiedasachievementsforwhichtheendstateisidenticaltothe beginningstate.Cyclicachievementscanbejuxtaposedwithdirectedachievements, inwhichtheactionresultsinachangeofstate.Forexample,theconceptHITcanbe classifiedasacyclicachievement,whileBREAKisclassifiedasadirectedachievement. Theprimarysubclassificationofactionconceptsforthisstudyisbasedonthese aspectualclasses. BuildingontheinsightsofFillmore(1970),BethLevin’s(1993) comprehensiveinvestigationofEnglishVerbsyieldsamorefinely-grained classificationthantheaspectualonedescribedabove.ByidentifyingVerbswith similarsyntacticbehavioracrossarangeofsyntacticalternations,sheisableto identifydozensofsemanticallyandgrammaticallycoherentsubclasses(e.g., “prepare”verbs,“chew”verbs,etc.).Levinandothershaveextendedthesefindings toincludelanguagesotherthanEnglish(fordiscussionofcross-linguistic applicabilityandrelevantcontributions,seeLevin,inpress).Ofcourse,notevery oneofLevin’sVerbclassescouldbeincludedinthisstudy.Iattemptedtoinclude exemplarsfromasmanydifferentLevin-inspiredverbclassesaspossible,andin particularthoseclasseswhosemembersarefrequentcross-linguistically. 14 Table4showsthesubclassificationofactionconcepts,withaspectualclasses representedinthefirstcolumnandLevin’sverbclassesrepresentedinthesecond column. Table4.Actionconceptsubclassificationandselectedexemplars Class 2-ParticipantStates InactiveActions DurativeProcesses CyclicAchievements (Semelfactives) DirectedAchievements Type Cognition Desire Emotion Perception Posture Spatial/Possession SocialInteraction Consumption Motion ChangeofState CreationVerbs Emission Contact BodilyMovement Existence ChangeofPossession ChangeofState Exemplars KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK 2.3. Typologicalmarkedness Theconceptoftypologicalmarkednesswasinvokedinthediscussionofparts ofspeechasprototypes.Inthissection,Iclarifythenotionofmarkednessasitis usedinthispaperanddiscussthecriteriathroughwhichitismanifestedin individuallanguages. Theterm‘markedness’wasintroducedbythePragueSchooloflinguistic theory(Trubetzkoy1931,1939/1969;Jakobson1932/1984,1939/1984,citedin Croft2003),andhassincebeenusedindifferentwaysbydifferentanalyticaland theoreticaltraditions.Inthispaper,Irefertomarkednessasitappliestocross- 15 linguisticuniversals,alsoknownas‘typologicalmarkedness’(Greenberg1966;Croft 1996).Croft(2003:87)summarizesmarkednessas“asymmetricorunequal grammaticalpropertiesofotherwiseequallinguisticelements:inflections,wordsin wordclassesandevenparadigmsofsyntacticconstructions.”Theseasymmetries aremanifestedinparticularlanguagesthroughdifferentcriteria,whichwillbe describedbelowandexemplifiedinSection3.3. Croftmakesitclearthattypologicalmarkednessis“auniversalpropertyofa conceptualcategory”ratherthanthepropertyofaspecificlanguageorconstruction (2003:88).Thisistruefortheapplicationofmarkednesstopartsofspeech.The conceptualcategoriesatstakearethecombinationsofbroadsemanticclassand propositionalactfunction,andasFigure1shows,threeofthesecombinationsare typologicallyunmarkedrelativetotheothers.Perhapscontroversially,Iwillalso use‘marked’/‘unmarked’terminologytorefertolanguage-specificmanifestationsof theuniversalpattern.Theselanguage-particularasymmetriesarealsoidentifiedby justthatterm—‘asymmetries’—aswellastheirrelevantcriteria.However,theterm ‘asymmetry’doesnotcapturethedirectionoftheasymmetry,whichisessentialto theconceptionofmarkednessatboththelanguage-specificanduniversallevels. Therefore,Iwilloftenrefertoalanguage-particularconstructionas‘marked’ relativetoaparallelconstruction.Itisthesenumerouslanguage-specificdirectional asymmetriesthatcollectivelyconstitutethepatternoftypologicalmarkednessfor thatcategoryanditscorrespondingvalues. Furthermore,markednessisessentiallyarelationbetweentwovalues.A constructionviewedinisolationcannotbedescribedasmarkedorunmarked; 16 rather,itcanbeidentifiedassuchrelativetoaparallelconstructionaccordingtoone ofseveralcriteria.ItisthesecriteriathatIturntonext. 2.3.1. Structuralcoding Onecriterionoftypologicalmarkednessisstructuralcoding,whichentailsan asymmetryintheformalexpressionoftwoormorevaluesofacategory(Croft 2003).Thisasymmetryisoneofquantity.Forstructuralcoding,thequantityisthe numberofmorphemesneededtoexpressthevaluesforthecategoryinquestion2. Structuralcoding:themarkedvalueofagrammaticalcategorywillbeexpressed byatleastasmanymorphemesasistheunmarkedvalueofthatcategory(Croft 2003:92) Zerocoding—thatis,theformalexpressionofavaluewithoutanovertmorpheme— isacommonstrategyformanycategoriesandmanylanguages.Alternatively,values canbeexpressedthroughovertcoding. Itisimportanttorememberthattheasymmetryisauniversalone,whichis notnecessarilyrealizedineachlanguage.Thephrasing“atleastasmany”inthe definitionaboveiskey.Croftusesthecategoryofnumbertoillustratethispoint. Singularisclaimedtobeanunmarkedvaluefornumberrelativetoplural.Indeed, forlanguagesinwhichthereisanasymmetryinthenumberofmorphemesneeded toexpressthesevalues,thepluralisalmostalwaysthevaluethatrequiresmore morphemes.Yetforsomelanguages,bothvaluessingularandpluralareexpressed equallyintermsofnumberofmorphemes—eithertheyarebothzero-coded,orboth 2Countingmorphemesanddeterminingwhichmorphemestocountarenot uncontroversialendeavors.ThereaderisreferredtoCroft(2003:ch.4)for discussion. 17 overtlycoded.Still,theselanguagesdonotviolatetheclaimthatsingularis unmarkedrelativetoplural.Evenforcategoriesinwhichthereisnoasymmetryina particularlanguage,thetypologicalmarkednesspatternremainsvalid. 2.3.2. Behavioralpotential Thesecondcriterionoftypologicalmarkednessisbehavioralpotential, whichreferstotheversatilityofavaluerelativetoothervaluesofacategory.This versatilitycanbeexpressedintwoways,correspondingtotwotypesofbehavioral potential.First,inflectionalpotentialreferstothemorphologicaldistinctionsthata valuecanexpress;avalueisrelativelyunmarkedifitcaninflectformorecrosscuttingcategoriesthanacomparablevalue. Inflectionalpotential:ifthemarkedvaluehasacertainnumberofformal distinctionsinaninflectionalparadigm,thentheunmarkedvaluewillhaveatleast asmanyformaldistinctionsinthesameparadigm(Croft2003:97) Amoresubtleinflectionalpotentialdistinctioncanalsobemadebasedonhowthe inflectionisaccomplishedmorphosyntactically.Fromleastmarkedtomost,the inflectionmaybeaccomplishedbyinternalchange,affixation,orperiphrasis.These assignmentsofmarkednessarebasedontokenfrequency(see2.3.3):suppletive inflectionsaregenerallyfoundonlexemesofhighfrequency,affixationcoversa widerangeoflexemefrequencies,andperiphrasticconstructionsareoften employedforlowfrequencylexemes. Second,distributionalpotentialreferstothesyntacticcontextsinwhicha valuecanoccur;avalueisrelativelyunmarkedifitcanoccurinmoresyntactic contextsthanacomparablevalue.Unfortunately,acomprehensiveinclusionof 18 distributionalpotentialisbeyondthescopeofthisstudy.Astraightforwardinstance ofdistributionalpotentialforpartsofspeechwouldbealanguagethatdoesnot allowpropertyconceptstobeusedinreference,forexample.However,Ifoundno explicitevidenceforthistypeofprohibitioninanyofthelanguagesofthisstudy.3 Therearemoresubtlewaysinwhichdistributionalpotentialinteractswithpartsof speech,buttheyarenumerousandlanguagedescriptionsarenotconsistentinthe inclusionandorganizationofthiskindofinformation.Forthesereasons,the analysisofmarkednesscriteriainthisstudyislimitedtostructuralcodingand inflectionalpotential. Asitappliestopartsofspeech,thecriterionofinflectionalpotentialmust alsospecifywhatcategoriesofinflectionarerelevant.Thegreatestinflectional potentialisfoundattheprototypes—prototypicalcombinationsofbroadsemantic classandpropositionalactfunction—anddifferentinflectionalcategoriesshowup ineachoftheprototypes.Table5liststheprototypicalinflectionalcategoriesfor eachpropositionalactfunction.Fromtheseprototypes,inflectionalcategoriescan extendvertically(intermsofCroft’sconceptualspaceinFigure1),applyingtothe useoftheotherbroadsemanticclassesinthesamepropositionalactfunction;or theycanextendhorizontally,applyingtotheuseofthesamebroadsemanticclassin theotherpropositionalactfunctions.Becausethemethodologyofthisstudyisto compareconcepts(representedbycorrespondinglexemesinparticularlanguages) toeachotherinidenticalcontexts,onlytheverticalextensionsofinflectional categoriesdescribedaboveareconsideredinthisstudy.Thecontextforcomparison 3Ifoundonlytheoccasionallackofinformationaboutwhetherornotitispossible. 19 ofconceptsisaparticularpropositionalactfunctioninaparticularlanguage,soonly theinflectionalcategoriesprototypicaltothatpropositionalactfunctionare relevant.Horizontalextensionsofinflectionalcategoriesareresidual;alexememay retaininflectionalpotentialfromitsmorphosyntacticexpressionintheprototype evenwhenitisusedinotherpropositionalactfunctions.Sincetheseinflectional categoriesarenotequallyavailabletoallconcepts(becausetheyhavedifferent prototypes),theyarenotappropriatemeasuresforcomparingacrossconcepts(see also4.3.2). Table5.Prototypicalinflectionalcategoriesforeachpropositionalactfunction(Croft toappear) Reference size number definiteness deixis case Modification degree indexation linker Predication voice aspect tense modality evidentiality speechact polarity indexation Tosummarize,markedvaluesofacategoryrequireequalormorestructural codingandexhibitequalorlessbehavioralpotentialthantheirunmarked counterparts.Inapartsofspeechstudy,whatarethecategoriesandvaluesrelevant formarkedness?Thecategoryistheconceptualcontinuumwhichwasdescribedin section2.2.Sinceitisacontinuum,intheoryitsvaluesareinfinite.Forthisstudy, however,thevaluesaretheselectedconceptualexemplars,andtheseare representedineachlanguagebythelexemesthatencodethoseconcepts.As describedinthepreviousparagraph,thecontextsforcomparingthesevaluesare 20 thepropositionalactfunctions.Themarkednessasymmetriesamongtheconceptual valuesareexpectedtobedifferentforeachpropositionalactfunction,sincea differentsubsetofconceptsisprototypicalineachpropositionalactfunction. 2.3.3. Motivationsfortypologicalmarkedness Typologicalmarkednesspatternsaremotivatedbybothtokenfrequency (Greenberg1966;Bybee1985;Croft2003)andthetug-of-warbetweeneconomy andiconicity(Croft2003:101-2).4Thestructureoflanguagerepresentsthe structureoftheworldashumansexperienceit(iconicity).Ontheotherhand, limitationsontimeandresourcesinhumancommunicationmotivatethe minimizationoflanguagestructure(economy).Frequencyprovidestheasymmetry onwhichtheseopposingforcescanact.Givenacategorywithtwovaluesdiffering infrequency,economycanbemaximizedwhilesimultaneouslyretaininga distinctionthatisiconicofthedistinctionintheworld:themorefrequentvaluecan bezero-coded,whilethelessfrequentvalueremainsovertly-coded.Thisisprecisely thepatternthatisfoundformanycategoriesinmanylanguages(butnottheonly oneofcourse;inmanyinstances,bothorneitherofthevaluesareexpressed overtly).Inflectionalpotentialcanbeexplainedinthesameway.Throughcrosscuttinginflectionalcapabilities,morefrequentvaluesdisplayversatilitythatis representativeoftheworld.Thesamecross-cuttinginflectionalcapabilitiesareless usefulforinfrequentvalues,sotheyarelostorfailtocrystallizeinthegrammar. 4Thediscussionhereofthemotivationsfortypologicalmarkednessisacursory one.Thereaderunfamiliarwiththisliteratureisreferredtotheworkscitedfora moredetailedaccount. 21 Theseideasaresummarizednicelyintheoft-quotedmantra,“grammarsdobest whatspeakersdomost”(DuBois1985:363). Thesameprinciplesareatworkshapingpartsofspeechsystemsdespitethe countlesscomplexitiesandinterveningfactorsthatexistwithinsuchabroad characterizationoflanguagestructure.Certainconcepts—basedontheirsemantic make-up—lendthemselvestouseinparticulardiscoursefunctionsasadirect reflectionofhowhumansexperiencetheworld.Becauseoftheirusefulness,these conceptsareusedmorefrequentlyincertainpropositionalactfunctionsthanothers. Iconicandeconomicmotivationsturnthisasymmetryinfrequencyintoan asymmetryinform. Nowthatitissufficientlyclearwhatismeantbytypologicalmarkedness,I mustreiteratethatmarkednessasymmetriesarethevariationuponwhichthe multidimensionalscalinganalysisisbased.Noteverydifferenceinformqualifiesas amarkednessasymmetry.Considerthefollowinghypotheticalexample:onelexical classofalanguagerequiresaparticularderivationalmonomorphemicsuffixin reference,whileanotherclassrequiresadifferentmonomorphemicsuffixin reference.Therelevantcriterionhereisstructuralcoding,butthesetwo constructionshavethesamenumberofmorphemes.Inmarkednessterms,theyare equal.Therearemanylinguisticanalysesforwhichanydifferenceinformis significant,butonlymarkednessasymmetriesarerelevantforthisstudy. 22 2.4. Sourcesoftheprototypestructures Theprototypestructuresofpartsofspeechemergefrommarkedness asymmetrieswithinandacrosslanguages. Themorphosyntacticdistinctionsmadewithinaparticularlanguageserveto divideuplexemesintoclassesandsubclasses.Foralllanguagesinthisstudy, morphosyntacticevidencewasfoundtomotivateatleastabasicthree-way distinctionfortheencodingofobjects,properties,andactions.Bothstructural codingandinflectionalpotentialplayasignificantroleinhowthesebroadsemantic classesarerealizedineachofthethreepropositionalactfunctionsofreference, modification,andpredication.Yet,thebasicdistinctionsmadeinjustonelanguage arenotenoughtofullyillustrateprototypestructures.Sinceeachlexemeis evaluatedforstructuralcodinginthreepropositionalactfunctions,thereisthe possibilityforsomestaggeringofcategorizationlinesacrosstheseenvironments. However,thedistinctionsthatformlexicalclassesandsubclassesinaparticular languageareoftenfairlypersistentacrosspropositionalactfunctions.Thatis, NounsofalanguagepatternlikeotherNounsinreference,andtheyalsopatternlike otherNounsinmodification.ItislessfrequentthattheclassofNounsthatdisplay particularmorphosyntacticcharacteristicsinreferenceissignificantlydifferent fromanotherclassofNounsthatdisplayparticularmorphosyntacticcharacteristics inmodification. However,inflectionalpotentialplaysanimportantroleinaddingtothe markednessstructurewithinaparticularlanguage.Evenasmorphosyntactic evidenceisfoundtomotivatebroadlexicalclasseswithinalanguage 23 (correspondingroughlytoobjects,properties,andactions)inflectionalpotential oftenprovidesfurtherdelineationswithineachoftheseclasses.Moreprototypical membersofeachoftheseclassescanexhibitinflectionalcapabilitiesthatdistinguish themfromlessprototypicalmembers.Unlikethebroadstructuralcoding distinctionsthatarereinforcedinmorethanonepropositionalactfunction,these inflectionalpotentialasymmetriesareoftenspecifictoaparticularpropositionalact functionanditsrelevantcross-cuttinginflectionalcategories.Thus,evenwithina singlelanguage,majorandminormarkednessdistinctionscanbeobserved.Even still,fullprototypestructuresarenotreadilyapparentuntilmultiplelanguagesare considered. Whendatafrommanylanguagesarecompiled,thegradienceofthe prototypestructuresemerges.Languagesmakedifferentdecisionsonwhereto drawthelinesbetweenbothbroadlexicalclassesandsmallersubclasses.Thereisa setoffunctionalpressuresthatareatworkinalllanguages,andthesefunctional pressuresmotivatesimilaritiesintheirlexicalclassifications.Yetwiththousandsof lexemes(oreventheforty-nineincludedinthisstudy),notwolanguageswillcome topreciselythesamelexicalclassifications.Languagesmoreoftenalignintheir classificationofsomelexemes,andtheseformtheheartsoftheprototypes; languageslessoftenalignintheirclassificationofotherlexemes,andtheseformthe peripheriesoftheprototypes.Itistheaccumulationofalltheselanguage-specific decisionsfrommanydifferentlanguagesthatresultsintherichprototypestructures illustratedinthisthesis. 24 2.5. Cuttinguptheconceptualspace 2.5.1. Semanticmaps Animportanttooldevelopedinlinguisticsforrepresentingcross-linguistic regularitiesinsemanticstructureisthesemanticmapmodel,exemplifiedinFigure 1.ItcametoprominencethroughearlyworkbyAnderson(1974,1982,1986,1987), andhassincebeenusedbyvariousscholarstorepresentdatafromawiderangeof linguisticdomains(e.g.,Croft,Shyldkrot&Kemmer1987;Kemmer1993;Stassen 1997;Haspelmath1997a,1997b,2003;vanderAuwera&Plungian1998;Croft 2003).FollowingCroft(2001:93),Iwillusetheterm‘conceptualspace’forthe language-universalconceptualstructureand‘semanticmap’forthelanguageparticularsemanticstructurethatcanbe‘mapped’ontotheconceptualspace.The strengthsofaconceptualspacelieinitsabilitytocapturesimilaritiesacrossmany languageswithoutassuminguniversallanguagecategoriesandtoillustratethose relationshipsbeyondasimplelinearstructure(Croft2001;Haspelmath2003). Whilesomeapplicationsofthesemanticmapmodelclaimonlyacharacterizationof theshapeofthedata,conceptualspace—asitsnameimplies—likelyreflectsa universalconceptualstructureinthemindsofhumanbeings(Croft2001:105, 2003:138-9). Therelationshipbetweenconceptualspaceandmarkednesspatterns realizedinstructuralcodingandbehavioralpotentialhasalsobeenexplicitly framed(Croft2003:140-2).Thiswouldsuggestthatthesemanticmapmodelcould beappliedtothepartsofspeechdatapresentedhere.However,thesemanticmap 25 modelfacessomeinherentchallenges.First,buildingaconceptualspaceisnoteasily tractablewithlargeamountsofdata.Second,thesemanticmapmodelisunableto dealwithexceptions.Currentresearchintypologyhasincreasinglycometo appreciatetheprevalenceandsignificanceofstatisticaluniversals,sotheabilityto dealwith‘messy’dataisquicklybecominganessentialrequirementoftypological models.Third,thesemanticmapmodelisnotmathematicallyformalized.Itsnodes andconnectionsfailtorepresentthedegreeofsimilaritybetweenrelatedconcepts. Theexistenceofaconnectionbetweentwoconceptsiscapturedinaconceptual space,butnotthestrengthofthatconnection.Fortunately,thereisa mathematically-formalizedalternativetothesemanticmapmodelwhichhasbeen adaptedforanddemonstratedonlinguisticdata. 2.5.2. Multidimensionalscalinganalysis Multidimensionalscaling(MDS)ismultivariatemethodthatdirectlymodels similarityindatabydistanceinaspatialmodel.Ithasonlyrecentlybeenadopted fromrelateddisciplinesforuseingrammaticalanalysis(Croft&Poole2008;further backgroundonMDScanbefoundinPoole2005:ch.1).MDSmodelshaveseveral advantagesoversemanticmapmodels.MDSisamathematicallyformalized approachthatcapturesnotonlythecategoricalrelationshipsamongdatapoints,but alsotheirdegreesofdissimilarity.Infact,MDSdeliversatrueEuclideanmodel.This isanadvantageofMDSoveralternativemethodsfordiscoveringstructurein multivariatedatasets,suchasfactoranalysisandprincipalcomponentsanalysis, 26 whichonlyattempttocapturemostofthevariationinthedata(foramoredetailed comparisonoftheseapproaches,seeCroft&Poole2008:12-13;Baayen2008:ch.5). NotonlycanMDSbeappliedtolargedatasets,themodelactuallyimproves withtheadditionofdata(Croft&Poole2008:31-2).Thedataonpartsofspeech corroboratesthisclaim.Aswasmentionedabove,theinclusionofelevenlanguages andnearlyfiftylexemesforeachlanguageturnedouttobejustenoughdatato producerecognizableprototypestructuresandillustratetheirinternalstructures. Thecodeddataforthisstudycomprisea49X169matrix.Bycomparison, Haspelmath’s(2003)pronoundata(asanalyzedbyCroft&Poole[2008])forma9X 139matrixandDahl’s(1985)tenseandaspectdataforma250X1107matrix. Finally,MDScanhandleexceptionsinthedata—thatis,itmeasuresthefitofthe modeltothedata.IturnnowtoabriefdescriptionoftheMDSspatialmodelthat willbeimportantforinterpretingtheresultsfromthepartsofspeechdata. AnMDSspatialmodelconsistsofanarrangementofdatapointsandlinear cuttinglines.Eachcuttinglinesignifiesadiscretebinarycategorizationofthedata, inthiscaseasinglegrammaticaldistinctionfromalanguage.Thedatapoints representtheconceptualtargetsusedinthisstudy.Asinglecuttinglinewilldivide someportionofthedatapointsfromtherest.Thepointsononesideofthislineare allthelexemesfromaparticularlanguagethatassumeamarkedforminaparticular propositionalactfunctions,whilethepointsontheothersideareallthelexemes thatassumetheunmarkedform.Itistheaccumulationofallthesecuttinglinesand theirdiscretecategorizationsthatforcethedatapointsintoaparticularspatial arrangementinthemodel.Specifically,thecuttinglinesformpolytopesthatcontain 27 theideallocationforeachdatapoint,butadatapointcanbeanywherewithinthat polytope.Moredataresultinmorecuttinglines,andmorecuttinglinesresultin moreaccuratepolytopes.Iftwopointsarecloseinproximityintheanalysis,itis becausetheyfallonthesamesideofacuttinglinemoreoftenthannot.Iftwopoints areverydistant,itisbecausetheyfallonoppositesidesofacuttinglinemoreoften thannot.Inthisway,theaccumulationofbinarycategorizations(cuttinglines) resultsinsimilarity(ordissimilarity)measuresamongallthedatapoints.Forparts ofspeech,thistoolprovidesamorenuancedillustrationofcross-linguisticpatterns. Likethesemanticmapmodel,anMDSanalysisoflinguisticdatais hypothesizedtocapturemorethanjustpatternsinthedata.Akeydistinctionis madebetweenthelanguage-andconstruction-specificnatureofthecuttinglines andtheuniversalnatureoftheresultingconceptualspace.Thisconceptualspace modeledbyMDSishypothesizedtobethesameforallspeakers.AccordingtoCroft &Poole,“therelativepositionanddistanceofpointsinthespatialmodelrepresenta conceptualorganization,presumablytheproductofhumancognitionand interactionwiththeenvironment,thatconstrainsthestructureofgrammar”(2008: 33).Itcanthenbeinferredthatthesemanticorfunctionalcategoriesreflectedinthe structureofthisconceptualspacearerelevanttogrammar.Inotherwords,the dimensionsofanMDSanalysisaremeaningful,andtheyallowforatheoretical interpretation. ThespaceintheMDSanalysisofpartsofspeechcouldplausiblyrepresent “conceptualdiscreteness”(Croft&Poole2008:20-1).However,basedonthe complexityofthedataandthetheoreticallyunlimitednumberofpossiblelexemes 28 acrosslanguages,itismorelikelythatthespacerepresentsacontinuumintowhich morelexemescouldfallshouldtheybeincludedintheanalysis.Croft&Poole(2008: 21)writethatwiththeaccumulationofmoredata,“whatatfirstappearstobea discreteconceptualspaceisrevealedtobeamorecontinuousstructureina conceptuallymeaningfulspatialrepresentation.” Thespatialmodelisconstructedbasedonboundariesratherthanprototypes (Croft&Poole2008:11),andthishasimplicationsfortheoreticalinterpretation. Prototypestructures—centersofsimilaritygravityaroundwhichpointscluster— mayshowupinanMDSanalysis,buttheyareanindirectproductofaccumulated dissimilarities.Therefore,mysubsequentdiscussionsofprototypestructuresinthe analysisofpartsofspeechdatashouldbeunderstoodinthislight.Thisfitsnicely withthelargerconceptionoflanguageuniversalsasindirectconstraintson grammaticalvariationratherthanuniversallinguisticcategories(Croft&Poole 2008:32).Thesemanticcontinuumfromobjecttopropertytoactionconcepts statisticallyconstrainsthepossibilitiesforlexicalcategorizationwithinalanguage, eventhoughnotwolanguageschooseexactlythesamecategoriesandboundaries. 29 3. Dataandmethodology 3.1. Selectionofthelanguagesample 3.1.1. Geneticandgeographicdiversity Figure2.Geographicdistributionofthelanguagesample Forthisstudy,datawascollectedfromelevengeneticallyandgeographically diverselanguages.Asconcernsgeneticrelatedness,onlylanguagefamiliesthathave beenthoroughlydemonstratedandacceptedbyamajorityofthelinguistic communityareconsideredhere.Accordingtothisstandard,notwolanguagesused inthisstudybelongtothesamelanguagefamily.However,morecontroversial claimsaboutlargerlanguagegroupings,suchasAmerind(Greenberg1960,1987; Ruhlen1991),havethepotentialtosubsumemorethanoneoftheselanguages. Geographically,threelanguageswereselectedfromeachofthreeregions—the 30 Americas,Africa,andAustralia/Oceania—andtwofromEurasia.Withineachregion geographicdiversitywaspreferred,withonenoteworthyexception:theinclusionof bothMali(Baining)fromEastNewBritainandToqabaqitafromtheSolomon Islands.Eventhoughtheyaregeneticallyunrelated,botharespokenonislandsjust totheeastofNewGuinea.Ultimately,thecombinationofgeneticandgeographic diversityyieldsafairlybalancedtypologicalsurvey. Table6.Summarydataforthelanguagesusedinthisstudy(geneticclassificationand populationnumbersarefromEthnologue) Region Africa Americas Australia/Oceania Eurasia Language Kanuri Khwe Kisi Creek(Muskogee) K’ichee’ Quechua(Huánuco) Jingulu Mali(Baining) Toqabaqita English Ingush Classification Nilo-Saharan Khoisan Niger-Congo Muskogean Mayan Quechuan Australian EastNewBritain Austronesian Indo-European NorthCaucasian L1Speakers 3,760,500 6,790 10,200 4,000 2,330,000 40,000 52 2,200 12,600 335,148,868 322,900 Admittedly,theavailabilityofdetailedreferencematerialswasaprimary factorintheselectionoftheselanguages.Formostofthelanguagesinthisstudy, descriptivegrammarsandtwo-waydictionarieswithEnglishwererequiredto collectthenecessarygrammaticalandlexicaldata.Manyofthesewere recommendationsfromexperiencedtypologists,andafewlanguagesoriginally includedwerereplacedduetotheinsufficientnatureoftheirreferencematerials. Typologicaldiversitywasconsideredintheselectionoflanguages,butnotall typologicalinformationwasexpectedtoberelevant.Forexample,thereisno expectedcorrelationbetweenwordorderandpartsofspeechstrategies. 31 3.1.2. Diversityinpartsofspeechstrategies Anotherformofdiversitythatisimportantforthisparticularstudyisthatof partsofspeechstrategies.Bythis,Iamreferringtoboththeclaimsmadeofsome languagesthattheydonothaveallthreebasicpartsofspeech,aswellthe generalizationsmadeaboutotherlanguagesbasedonperceivedsimilarities betweentwoofthethreemajorpartsofspeech.Languages“withoutadjectives”are themostcommonexampleofthefirsttype.Additionally,therearelanguagesfor whichithasbeensuggestedthattheNoun/Verbdistinctiondoesnotexist.The secondtypeincludeslanguageswith“VerbalAdjectives”or“AdjectivalNouns”(see section2.2.2).ThegeneralizationhereisthatthelexicalclassofAdjectivesina particularlanguagesharesasignificantamountofmorphosyntacticpropertieswith eitherNounsorVerbs,insomecasestotheextentthattheymightbeconsidered onelargelexicalclasswithtwosubclasses.Bothtypesarerepresentedinthisstudy. Anothercross-linguisticvariableisthesizeoftheAdjectiveclass(also discussedin2.2.2).Again,thelanguagesusedinthisstudycovertherangeof inventorysizes,fromalanguagewithone‘true’Adjectivetootherswithverylarge Adjectiveclasses. Itwouldbedifficulttogroupthelanguagesofmystudydefinitivelyintothe typesdescribedabove.Whileseveralofthegrammarauthorsfortheselanguages makesuchclaims,thestrengthsoftheclaimsvary,asdoestheamountofevidence thatisprovidedbytheauthorstosupporttheirclaims.Toillustratethediversityof thelanguagesampleintermsofpartsofspeechstrategies,Ihaveincludedrelevant 32 excerptsfromthelanguageresourcesinTable7.However,itcannotbeunderstated thatmarkednessasymmetriesofatleastonetypewerefoundtodistinguishall threesemanticclasses(objects,properties,andactions)ineverylanguageinthe study.Thatis,therewasnolanguageforwhichIfailedtofindmorphosyntactic evidencetodistinguishatleastthreebasicpartsofspeechfromeachother. Table7.Selectedinsightsintothenatureofpartsofspeechfromtheauthorsofthe referencematerials Language Kanuri PartsofSpeechDescription "Wordsthattranslateintootherlanguagesasadjectives…havenoovert phonological,morphologicalorsyntacticcharacteristicsthatdifferfromthesame characteristicsofwordsthattranslateintootherlanguagesasnouns."(Hutchison 1981:35) Khwe "Khwedoesnothaveacoherent,properwordclassofadjectives…Itformally distinguishesbetweenverbaladjectives,adjectivesderivedfromnouns,adjectives relatedtopronouns,andnumerals”(Kilian-Hatz2008:195) Kisi "Adjectivesareaneasilydefinedclassconsistingofwordsattributiveinmeaning andshowingagreementwithnounstheymodify."(Childs1995:151) Creek "StemsinCreekcanbedividedintoseveralclassesbasedontheirgrammatical (Muskogee) behavior"(Martin2011:29) Quechua "Thereisinsufficientevidenceofastrictlymorpho-syntacticnatureforconsidering (Huánuco) thatnounsandadjectivesformdistinctlexicalclasses."(Weber1989:9) Jingulu "Australianlanguagesoftendonotshowanymorphosyntacticcontrastbetween nounsandadjectives…InJingulu,however,thereappeartobesomereasonsfor consideringnounsandadjectivestobeatleastpartiallydistinctpartsofspeech." (Pensalfini2003:57) Mali(Baining) "Itcanbedifficulttodistinguishadjectivesfromverbssinceanadjectivemayhead anintransitivepredicate."(Stebbins2011:95) Toqabaqita Theclassofadjectivesconsistsofonemember(Lichtenberk2008b:52,230) Ingush Ingushhasthreemajorclassesofwords:nouns,verbsandmodifiers(Nichols2011: 113) 3.2. Datasources Threedifferentmethodswereusedinthecollectionofdatafromeleven languages.Fornineoftheelevenlanguages,datawascollectedfromgrammarand dictionaryreferences.Asmentionedabove,thequalityofreferencematerialswas importantintheselectionoflanguagesforthestudy.Evenso,challengesarosewith thismethodofdatacollection,andtheyarediscussedlaterinthispaper.Asanative 33 speaker,self-elicitationandpersonalgrammaticalityjudgmentswereusedfor English. TheK’ichee’Mayandatainthisstudywerecollecteduniquelythrough elicitation,soIdescribethemethodshere.MuchoftheelicitationwasfromJames Mondloch,ProfessorofK’ichee’attheUniversityofNewMexico.Forcertain constructions,hedeferredtohiswife,MariaMondloch,whoisanativespeakerof thelanguage. Themethodsofelicitationevolvedthroughoutthecollectionofthedata. Beforeanyelicitation,abriefoverviewofthisstudywaspresentedtoDr.Mondloch. Itwasexplainedthattheanalysiswasofparticularlexemesandhowtheselexemes areencodedineachofthetargetedpropositionalactfunctions.Originally,example sentenceswerepresentedinEnglishwiththelexemeappearinginallcapitalsinits unmarkedform: <<WHITErepresentspurity>>(reference) <<TheWHITEdogbarked>>(modification) <<ThecatWHITE>>(predication) ThegoalofthiselicitationstrategywastoavoidabiastowardEnglishconstructions, particularlyforthenon-prototypicalpairingsofsemanticclassandpropositionalact function.Forexample,ifagerundformwasusedintheelicitationpromptforan actioninreference,itmightbiasthespeakertouseananalogousforminK’ichee’at theexpenseofotherpossiblemorphosyntacticstrategiesforthiscombination. Ultimately,thismethodwasinsufficient.Formanycombinationsoflexeme andnon-prototypicalpropositionalactfunction,thebare(un-derived)formofthe lexemecreatedconfusionastothemeaningofthesentence.Toclarifytheintended 34 elicitation,thelexemeshadtobegiveninfullygrammatical(derived)forminthe Englishprompts.ForEnglishpromptsforwhichthereismorethanonepossible morphosyntacticstrategy,allrelevantstrategiesweregivenintheelicitation.For example,Englishhasmultiplestrategiesforencodinganactionwordinmodification, namelyaParticipleformandaRelativeClauseconstruction.Bothofthesewere giveninEnglishtoalleviatebiasinelicitingthesamecombinationinK’ichee’: <<TheFIGHTINGmanshouted>>(Participle) <<ThemanWHOFIGHTS/WHOWASFIGHTINGshouted>>(RelativeClause) Thisrevisedelicitationstrategywasmuchmoreeffectiveandwasresponsiblefor mostoftheK’ichee’data. ThedifficultyoftheelicitationprocessforK’ichee’actuallysupportsthe theoryofpartsofspeechtowhichthispaperascribes.Onthesurface,frequencycan explainwhycertaincombinationsofsemanticclassandpropositionalactfunction aredifficulttoartificiallyconstructfortherespondent.Themoreaparticular constructionispracticed,themoreeffortlessandnaturalitbecomestoproduce.As wasdiscussedinsection2.3.3,somecombinationsofsemanticclassand propositionalactfunctionareveryuncommon.Furthermore,theelicitationofa particularlexemeinoneoftheseuncommoncombinationsmaybeaskingthe respondenttoproduceaformofthatlexemeneverproducedbefore.Forexample, it’sverypossiblethatanativespeakerofK’ichee’hasneverheardorproducedthe abstractreferentialderivedformofROUNDequivalenttothe(ratherawkward) Englishround-ness.Therespondentwilllikelybeabletoproducesuchaformbased onanalogywithotherlexemes,buthesitationand/ordoubtastothegrammaticality ofthenewconstructionwouldbeunderstandable.Infact,thisreactionwaswell 35 attestedinmyelicitationoftheK’ichee’data.Formanyofthenon-prototypical combinations,therespondentsmadecommentstotheeffectof,‘Idon’tknowifI’ve everheardthatbefore.’ 3.3. Attestedpatternsofmarkedness Thepurposeofthissectionistopresentexamplesofasymmetryin markednessfromthelanguagesinthisstudy.Theseasymmetries—capturedinthe datacodedformultidimensionalscaling—arewhatdifferentiatetheconceptsinthe spatialmodel.Theasymmetriesarerealizedinbothstructuralcodingand inflectionalpotential.Theywillbepresentedforeachpropositionalactfunctionin turn.Notalltheasymmetriesinmarkednessthatwerefoundinthedataare presentedhere.Rather,thissectionfeaturesasubsetoftherelevantdatato illustratetheobservationsofmarkednessthatwerefoundamongtheeleven languagesofthisstudy.5 3.3.1. Reference AnexampleofstructuralcodinginreferencecomesfromIngush.Abstract NounscanbederivedfromAdjectiveswiththesuffix–al.Severalexamplesofthis derivationareillustratedin(1). 5Manyexamplesareincludedinthispapertoillustrateitsfindings.Allincludean interlinearmorphemetranslation.Foralltheselanguages,examplesarereproduced hereusingthesamephonetic,phonemic,ororthographicscriptusedinsource material.Noattemptwasmadetoconvertthedatatoastandardtranscriptionsuch astheInternationalPhoneticAlphabet.Tomyknowledge,thisdecisionbearsno impactonthemorphologicalandsyntacticvariablesthatareatissue. 36 (1) AbstractNounderivationinIngush(Nichols2011:158) Adjective: AbstractNoun: dika‘good’ dikal‘goodness’ d.aza‘heavy’ dazal‘heaviness,weight,value’ q’eana‘elderly’ q’eanal‘oldage’ q’uona‘young’ q’uonal‘youth’ Thisderivationevenworksforthewordeghazal‘anger’fromtheverbeghaz-lu‘get angry’,eventhoughthereisnocorrespondingadjectiveformfor‘angry’.Therefore, withregardtostructuralcoding,membersoftheIngushAdjectiveclassaremarked inreferencerelativetomembersoftheNounclass. TheinflectionofnumberonSubjectMarkersinToqabaqitaillustratesthe effectofanimacyonmarkednessofobjectsinreference.Inthethirdperson,higher animatenounsarealmostalwaysindividuated,anddualandpluralSubjectMarkers areused.Ontheotherhand,asingularSubjectMarkerisoftenusedforevendual andpluralmentionsoflesseranimatesandinanimates.Thereareexceptionstothis ruleinbothdirections. (2) AnimacydistinctionsinSubjectMarkernumberagreementinToqabaqita (Lichtenberk2008:150) a. botho baa ki kera oli na=mai pig that PL 3PL.NFUT return PRF=VENT ‘Thepigshavecomeback.’ b. fau neqe ki qe kuluqa stone this PL 3SG.NFUT be.heavy ‘Thesestonesareheavy.’ Furthermore,pronounsarenotoftenusedwhenthereferentisnothuman (Lichtenberk2008:72).Thesedistinctionsininflectionalpotentialserveto disambiguatesomeobjectconceptsfromothersinToqabaqita. 37 3.3.2. Modification InthissectionIpresentsomeexamplesofmarkednessdistinctionsin modification.AstraightforwardexamplecomesfromKanuri,inwhichNounsare overtlymarkedwithasuffixwhenusedinmodification.Forexample,the-másuffix in(3)indicatesarelationshipoftrade,profession,ororigin,amongothers. (3) KanuriNounsinmodification(Hutchison1981:196-7) kâm fə̀ r-má person horse-DENOM ‘personowningahorse’ ModificationconstructionsinToqabaqitademonstratehowlabelssuchas Noun,Adjective,andVerbdonottellthefullstoryofmarkednessdistinctions. AccordingtoLichtenberk(2008),ToqabaqitahasonlyoneAdjective.Thisanalysisis basedoninflectionalpotential:thissoleAdjectiveistheonlylexemethathas differentformsbasedonnumber,animacy,andcount/massnounstatus.Asaresult, mostpropertywordsinToqabaqitaarecategorizedasVerbs.Yet,thesemantic distinctionbetweenpropertiesandactionswithinthisclassofVerbsisstillrealized instructuralcoding.OnlyVerbswith"astativemeaningintheirsemanticrange" (327)canbeusedasmodifierswithoutanyadditionalstructuralcoding. (4) ToqabaqitaStativeVerbsinmodification(Lichtenberk2008:328) toqa maamaelia toqa suukwaqi toqa leqa peoplebe.powerful people be.strong people be.good ‘powerfulpeople,strongpeople,goodpeople’ AllotherToqabaqitaVerbscanonlymodifyreferentsinarelativeclauseintroduced bytheparticlena.Theplacementoftherelativeclausealsoservestodifferentiate thistypeofmodificationfromthestativeVerbmodifiers. 38 Evenmore,ToqabaqitaNounscanmodifyotherNounsinagenitive relationshipwithapossessivesuffixorsimplejuxtaposition(Lichtenberk2008b:ch. 8). (4) Toqabaqitagenitiveconstruction(Lichtenberk2008b:385) kaleko faalu clothes be.new ‘thechild’snewclothes’ wela child Insummary,itispossibleforallToqabaqitaobjectandpropertywordsto assumeamodificationrolewithoutanyaddedstructuralcoding.Onlythenonstativeactionwordsrequireadditionalstructuralcodingtodothesame.Despitethe classificationofalmostallpropertywordsasVerbsinToqabaqita,amarkedness distinctionremainsbetweenproperty-like(stative)Verbsandaction-like(nonstative)Verbs.ThesamelargeVerbclassgivesa“verby”impressionoftheproperty wordsinToqabaqita,yetitisobjectwordsthatpatternwiththepropertywordsas unmarkedinmodificationwithregardtotheirstructuralcoding. Havingdemonstratedhowmarkednessdifferencesmanifestinstructural coding,Iturnnowtoinflectionalpotentialinmodification.TheAdjectiveclassin Ingushmakesdistinctionswithinthebroadsemanticclassofpropertywordsbased ontheirinflectionalpotentialinmodification.Onlytwopropertywords—‘big’and ‘small’—agreeinnumberwiththeIngushNoun.Furthermore,lessthantenpercent ofAdjectivesagreeingenderwiththeNoun. (5) NumberandgenderagreementinIngush(Nichols2011:219-21) a. v-oaqqa sag V-big man ‘oldman’ 39 b. b-oaqq-ii nax B-big-PL people ‘elders’ TheinflectionalcapabilitiesoftheseAdjectivessetthemapartfromothermembers oftheIngushAdjectiveclass. 3.3.3. Predication Structuralcodinginpredicationistypicallymanifestedintheformofa copulaforobjectsand/orproperties.Forsomelanguages,suchasKanuri,nocopula isnecessary.Actions,objectsandpropertiescanbepredicatedbyjuxtapositionto thesubject. (6) PredicationofobjectsandpropertiesinKanuri(Hutchison1981:10-1) a. Álì bàrèmà Ali farmer ‘Aliisafarmer.’ b. Fə̂ r-nzé kúrà. horse-3SG.POSS big ‘Hishorseisbig.’ Forotherlanguages,thecopulaisrequired.InMali,thecoordinatorda‘and’ functionsasacopulaforthepredicationofproperties.Thisconstruction distinguishespropertiesasmoremarkedthanactionsinpredication. (7) PredicationofpropertiesinMali(Stebbins2011:49-50) chēvicha da aululka kēvi=ka da aulul=ka CONTR=M.SG.III and tall=3SG.M.III ‘Thatguyistall.’ AuniquepatternofmarkednessisfoundinJingulupredicationandit implicatesstructuralcoding.TheJingulupredicateconsistsofamandatoryLight 40 VerbandanoptionalCoverbalRoot.Theleastmarkedlexemesinpredicationare theLightVerbsthemselves,sincetheyoccurwithminimalstructuralcoding.The CoverbalRootsaremoremarkedbecausetheyonlyoccurwithanadditional morpheme—aLightVerb. (8) VerbalelementsofJingulu(Pensalfini2003:59-60) a. ya-jiyimi bininja 3SG-come man ‘Themaniscoming.’ b. ngaba-nga-ju karnarinymi have-1SG-do spear ‘Ihaveaspear.’ c. ngaba-nga-rriyi karnarinymi have-1SG-will.go spear ‘I’lltakeaspear.’ TheuniquewayinwhichMaliVerbsinflectfortenseinpredication constructionsresultsinseverallevelsofmarkedness.Theinflectionalcapabilityofa MaliVerbdependsonbothitsclass(A-D)andwhetheritis“StativeIntransitive”or “ActiveIntransitive”/“Transitive”(Stebbins2011:53-7).TheVerbclassesare differentiatedbytheirphonologicalformsandtheabilityoftheverbtoinflectfor tensedirectly:ClassAVerbsencodethreetenses,ClassBandCVerbsencodeonly thepresent/non-presentdistinction,andClassDVerbsdonotencodeanytense distinctions.Thestativityandtransitivitycategoriesarerelevantbecausethey determinewhichConcordialPronounaccompaniestheVerb;StativeIntransitives requireaClassIIIConcordialPronounwhichdoesnotencodeapast/non-past distinctionlikeotherConcordialPronounclasses. 41 Sinceboththeabilitytoinflectandthenatureofthatinflection(ablaut, affixation,orperiphrasis)arerelevantfactorsinmarkedness,MaliVerbsfeaturefive levelsofmarkedness. (9) LevelsofmarkednessintenseinflectionofMaliVerbs,fromleastmarkedto mostmarked(Stebbins2011) a. ClassA:Encodepast/present/futureonVerb b. ClassB+C(Transitive/ActiveIntransitive):Encodepresent/non-present onVerb,andpast/non-pastonConcordialPronoun c. ClassB+C(StativeIntransitive):Encodepresent/non-presentonVerb, butnopast/non-pastdistinctionduetouseofClassIIIConcordial Pronoun d. ClassD(Transitive/ActiveIntransitive):Nopresent/non-present distinctiononVerb,butencodepast/non-pastonConcordialPronoun e. ClassD(StativeIntransitive):Nopresent/non-presentdistinctionon Verb,andnopast/non-pastdistinctionduetouseofClassIIIConcordial Pronoun ThethirdlevelofmarkednessfeaturingClassesBandCStativeIntransitiveVerbsis notrepresentedbyanyofthelexemesinthisstudy,buttheotherfourlevelsare attested.Table8mapstheselevelsontotheintersectionofVerbClassand transitivity/stativity. Table8.TensedistinctionsofMaliVerbsaccordingtoVerbClass,transitivity,and stativity(basedondataanddescriptioninStebbins2011) Transitive ActiveIntransitive StativeIntransitive ClassA PST/PRS/FUT ClassB PST/PRS/FUT(partiallyperiphrastic) PRS/NPRS ClassC ClassD PST/NPST NoTenseDistinctions Inflectionalpotentialalsoyieldsmarkednessdistinctionsinpredicationin Creek.AspectandotherdistinctionsaremadeontheCreekVerbviaroot 42 alternations(“grades”)andsuffixes.Creekhasaprimarydistinctionbetween temporaryevents(Non-Duratives)andstates/prolongedevents(Duratives).All unmarkedVerbs(includesAdjectives,consideredasubclassofVerbs)inCreekrefer toanevent,whileVerbswithaDurativesuffixcanbeeithereventiveorstative (Martin2011:248-51).Manyoftheactionconceptsinthisstudyarespecifically non-eventive(e.g.,KNOW,WEAR)andwouldrequirethisDurativesuffix—marked structuralcoding—inordertoaccomplishanon-eventivemeaninginpredication. Thiswouldindicateamarkednessdistinctionbetweeneventiveandnon-eventive conceptsinCreek. However,thereisanotheravailableconstructionforconceptsthatfall somewherebetweennon-eventiveandprototypicallystative:“Verbsreferringto dressing,knowledge,perception,andholding…commonlyoccurintheresultative stativeaspect”(244).TheResultativeStativeiscloseinmeaningtotheDurative,but therearesubtledifferences;theResultativeStativeisusedforeventsthatare portrayedasshorterinduration,whiletheDurativeisusedforlongereventsor thosethataremoreneutralinthisregard(247).TheformoftheResultativeStative featuresafallingtone,butnosuffixliketheDurativeform.Therefore,theselexemes areunmarkedinpredicationjustliketheeventiveconcepts. InTable9,thecellswithboldtextaretheaspectualformsthatmatchthe conceptualtargetsofthisstudy.Both‘know’and‘wear’representconceptsincluded inmystudy.Whiletheyarenotconceptsincludedinmystudy,‘ripe’and‘bark’are usedheretorepresentphysicalpropertiesandcyclicachievements,respectively; 43 thiswasamatterofconvenience,astheCreekformsfortheselexemeswerereadily available. Table9.AspectualdistinctionsinCreekVerbs(fromdataanddescriptioninMartin 2011) Non-Durative Eventive (LengthenedGrade) 'lo:kc-ís 'it'sgettingripe' ki:ll-ís 's/heislearningit' ResultativeStative (FallingGrade) ‘wear’ 'a:cc-ís 's/heisputtingon' â:cc-is 's/heiswearing' ‘bark’ 'wo:hk-ís 'it'sbarking' ‘ripe’ ‘know’ kîll-is 's/heknows' Durative (ZeroGrade+ DurativeSuffix) lókc-i:-s 'it'sripe' kíll-i:-s 's/heknows'or's/heis knowledgeable' ácc-i:-s 's/hehason'or's/he would/couldwear' wo:hk-í:-s 'itbarks(allthetime)' 3.4. Codingthedata Themultidimensionalscalinganalysisusedinthisstudywasaccomplished withtheMDSOCRprogram,developedbyKeithPooleandmodifiedslightlyby JasonTimmsforuseonlinguisticdata.ThisprogramfeaturesPoole’sOptimal Classificationalgorithm,anonparametricbinaryunfoldingalgorithmwhich progressivelyapproximatesdatapointsandcuttinglinesuntilanoptimal classificationisreached(Poole2000,2005). TheMDSanalysisisconstructedfrombinarydata,andeachcolumnofdatais responsibleforonecuttinglineintheanalysis.Eachcolumnrepresentsonelevelof markednessattestedforatleastonelexemeinaparticularlanguageand propositionalactfunction.Theforty-nineconceptualtargets(representedby lexemesineachlanguage)constitutetherows,andtheyarecodedinthatcolumn 44 foroneoftwovaluesindicatingwhetherornottheyoccuratthatlevelof markednessinthatlanguageandpropositionalactfunction. Iwillillustratethiswithanexample.Onecolumnofmydataislabeledwith thefollowingshorthand:“KI-R-SC-Affix”.Theshorthandcontainsfourcomponents separatedbydashes.Thefirstcomponentisatwo-lettercodeindicatingthesource language,inthiscaseKIforKisi.Thesecondcomponentindicatesthepropositional actfunctioninquestion(R=reference,M=modification,P=predication).Thethird componentindicateswhetherthemarkednessdistinctioninquestionpertainsto structuralcoding(SC)orbehavioralpotential(BP).Thefourthcomponentcan indicatethetypeofstructuralcoding,or—forthecriterionofinflectional potential—thecategorybeinginflectedfor.Fortheexampleabove,“Affix”isthe amountofstructuralcodingnecessaryforthelexemetooccurinthatpropositional actfunction.Thereareafewcolumnlabelsthatincludeafifthcomponent, necessitatedbydifferencesinhowinflectionalpotentialisaccomplished,eithervia internalchange,affixation,orperiphrasis(see2.3.2). Athirdcodingoptioncanindicatewhenthedataisnotavailable.Thismay resultfromanyofanumberofscenarios.Forsomelanguages,aparticularlexeme couldnotbefoundtomatchtheconceptualtarget.Thatmeantthelexemewas assignedthisvalueacrossallthecolumnsforthatlanguage.Inothercases,the grammarwriterwasexplicitthats/hehadnoinformationregardingacombination oflexeme(s)andpropositionalactfunction(s).Finally,thereweresomerelevant constructionsthatwereleftunclearorunmentionedbythereferencematerial. 45 Fortunately,theMDSalgorithmusedherecanadeptlyhandlethesemissingdata points. Inmanyinstances,variousinflectionalabilitiespatterntogether.Forexample, asetofactionwordsmaytakeonseveralinflectionalabilitiesinpredication,suchas tense,aspect,mood,etc.Ifalltheseinflectionalabilitiesapplytothesamesetof lexemes,theyaretreatedtogetherinonecolumnofcodeasalevelofmarkedness (whichatleastsomeotherlexemesdonotdisplay).Thiswasasteptoavoid redundancy;representingeachinflectioninitsowncolumnwouldhaveresultedin manyidenticalcuttinglines. 3.5. Dimensionality OncethedataiscodedandtheMDSanalysiscomplete,thereisstillanother theoreticaldecisiontomake.TheappropriatenumberofdimensionsforanMDS analysisisdependentonthepropertiesofthedata.Theanalysisisperformedinone, two,andthreedimensions,andfitnessstatisticsareprovidedtoindicatethelevelof fitforeachnumberofdimensions.Complexdataresultinaninverserelationship betweeninformativenessandgoodnessoffit:asmoredimensionsareincluded, goodnessoffitincreasesbuttheinformativenessofthemodeldecreases.Thebest modelisthelowest-dimensionalmodelthatoffersarelativelyhighdegreeoffit,and forwhicheachadditionaldimensionoffersonlysmallimprovementsinthefit(Borg andGroenen1997:ch.3&4,citedinCroft&Poole2008:12).Fordatainwhich languageuniversalsarepresent,alow-dimensionalmodelwithrelativelygood 46 degreeoffitisexpected.Thelimitednumberofdimensionscanthenbeassociated withasmallsetoffunctionalfactorsthatareresponsibleforthevariation. 47 4. Methodologicalchallenges Beforeapresentationanddiscussionoftheresultsofthisstudy,itis importanttobringattentiontosomeofitsmethodologicalandtheoretical challenges.Whilethesechallengesdonottakeawayfromthesignificanceofthe results,theyappropriatelyqualifythem.Forfuturestudiesinthisvein,thissection offerspotentialareasforcriticalthoughtandcreativesolutions. 4.1. Cross-linguisticcomparison Apersistentchallengeinthediscipline-widetypologicalendeavoristhatof cross-linguisticcomparison.Infact,thetheoryoflexicalwordclassesmaintainedin thisstudyrejectstheuniversalityofformalwordclasses.Aswehaveseen,aword classsuchasAdjectiveinaparticularlanguageisnotequivalentorcomparableto theAdjectivewordclassinanotherlanguage.Yetthisstudyattemptstorelatethe formalwordclassdistinctionsmadeineachlanguage—includingvarious constructionsandgrammaticalcategories—tothedistinctionsmadeintheother languagesofthestudy.Thesolutionistocomparelexemesonlytootherlexemesin thesamelanguageandthesamepropositionalactfunction.Alexemeinaparticular languageisnevercomparedtoalexemewiththesamemeaninginanotherlanguage. Onlythelexemeswithinalanguagearecomparedtoeachother,andmeasuresof dissimilarityaresummedcumulativelyacrossconstructions,propositionalact functions,andlanguages.Sooveralldissimilaritymeasuresamongconceptsarethe cumulativedissimilaritiesofmanyintra-languageandwithin-propositionalact functioncomparisons. 48 4.2. Scope Theprimarylogisticalchallengeofthisstudyisitscombinationofambitious scopeandattentiontodetail.Collectionofdatainvolvedcombingthrough thousandsofpagesofgrammarsanddictionariestofindtherelevantinformation fornearlyfiftylexemes,inthreepropositionalactfunctions(andfortypicallymore thanonepossibleconstructionineachofthesecombinations),andforeleven languages.Yetallthesewerenecessary,andtheinterestingresultsofthisstudy wouldlikelybecompromisedifnotfortheirinclusion.Theapproximatelyfifteen conceptsforeachbroadsemanticclassarejustenoughtoexploretheinternal structureofeachprototype.Allthreebroadsemanticclasseshadtobeincluded, sinceitisthebehaviorofconceptsbetweentheprototypes—relativetothoseator neartheprototypes—thatrepresentarguablythemostinterestingdatapoints. 4.3. Theoreticalconstraints 4.3.1. Distributionalpotential Asdiscussedinsection2.3.2,distributionalpotentialisnotcodeddirectlyin thisstudy.However,itinfluencesothermarkednesspatternsthatareincluded. 4.3.2. Non-prototypicalinflectionalpotential Itisimportanttoremindthereaderwhatisnotcapturedinthisstudy.In section2.3.2,Iindicatedthatinflectionalpotentialcanextendfromtheprototypes 49 totheotherbroadsemanticclassesortootherpropositionalactfunctions.Thefirst oftheseisincludedinthescopeofthisstudy.Thesecond—inflectionalpotential thatisnotprototypicalforthatpropositionalactfunction—isnotincluded. Forexample,tenseinflectionisprototypicalforpredication,andCreekisno exception:itfeaturesafullsetoftenseinflectionsinmainclauses(actionsin predication).Relativeclauses(actionsinmodification)havethesametense distinctionsasmainclauses(Martin2011:398),butcomplementclauses(actionsin reference)havefewertensedistinctions(390).Asimilarphenomenoncanbefound inToqabaqita.Whenusedinmodification,ToqabaqitaVerbscanretainsome inflectionalcapabilitiesprototypicalforpredication;yettherearealsosomeverbal particlesthattheycannottakeinthisconstruction.In(10),theVerbmae‘bedead’is modifying‘man’andisinflectedforperfectiveaspect. (10) Toqabaqita(Lichtenberk2008b:331) kafa qeri, wane mae naqa n=e comb this man be.dead PRF FOC=3SG.NFUT alu-lu-a own-RDP-3SG.OBJ ‘Thiscomb,itwasthedeadman(lit.:thenow-deadman)whousedtoownit.’ Thesepatternsmatchwhatisexpectedbytheprototypetheoryofpartsofspeech; asthecombinationoflexical-semanticclassandpropositionalactfunctionmove furtherfromprototypicality,markednesswillstaythesameorincrease(inthiscase, lostinflectionalpotential).However,thesedistinctionsarenotcapturedinthis studybecausetenseandaspectarenotprototypicalinflectionalcategoriesfor modificationorreference. 50 4.4. Practicalconstraints 4.4.1. Availabilityofdata Evenwithexcellentgrammarsandextensivedictionariesatmydisposal,the availabilityofdatawasachallengethroughoutthestudy.Afewexampleswillsuffice toillustratethesechallenges.6InKisi,forexample,thegrammarindicatesthatthe "o"nounclassisunmarkedonthenoun(nosuffixednounclasspronoun) (Childs:1988:231),yetthedictionaryforKisidoesnotlistthenounclassforeach nounentry.TheMaligrammar,despiteitsclarityandorganization,doesnotgive anyinformationabouthowAdjectivesorVerbsareusedinreference.Some grammarsweremoreexplicitincertaindomains;theKisireferencegrammarleft outdiscussionofthegenitiveduetoitsintendedfocusonthephonologyand morphologyofthelanguage. 4.4.2. Generalizabilityanddetail Thenatureofgrammarsanddictionariesadditionallypresentstheproblem ofcategorizationandgeneralization.Forthisstudy,itisoftentheexceptionstothe rulesthatrepresentthemostinterestingdatapoints.Yetgrammarsanddictionaries mustnecessarilygeneralizeinordertocaptureasmanyimportantfactsabouta languageaspossiblewithinreasonableconstraintsoftimeandspacesetby themselves,publishers,andpracticality.Ifoundoccasionalexamplesinmyresearch 6Theseexamplesareinnowayintendedtocriticizetheauthorsofthesource materials.Onthecontrary,thetypologicalnatureofthisresearchisonlypossible duetothediligenceoffieldworkersanddescriptivelinguists.Thegrammarsand dictionariesutilizedwerechosenfortheirhighquality. 51 thatindicateaninconsistencyamonglexemesassignedtothesameclassorcategory. AninterestingexamplecomesfromK’ichee’.Thesuffixes–iiland–aaloftenmake AbstractNounsoutofAdjectives,suchasutz-iil‘goodness’fromutz‘good’,nim-aal ‘bigness’fromniim‘big’,andk’a’n-aal‘angriness/anger’fromk’a’n‘angry’.However, ketekik‘round’yieldsketekik-iil‘spherical’—anAdjectivejustlikeitsstembutwith anaddedspatialdimensioninitsmeaning.Inthiscase,generalizationsregarding thesuffixfailtocapturewhatconstitutesasignificantdistinctioninmyanalysis. Infact,thelanguagesthataremostinterestingforthiskindofstudyarethose thatmakevariousdistinctionsamongandwithinmajorlexicalclasses.Thosesame languages,andinparticulartheirvariousgradations,arethemostdifficultforthe descriptivelinguisttoaccuratelydescribewithoutextremelydetailedattentionto theuniquepropertiesofindividualwordsandconstructions. 4.4.3. Gradienceingrammaticality Notsurprisingly,theelicitationofK’ichee’Mayandatabroughtsomeunique insights.Themostimportantoftheseisthegradientnatureofgrammaticality.AsI havearguedabove,thebroadsemanticclassesarenotdistinguishedcategorically, butthelexemesoftheseclassesfallonacontinuumbasedonsemanticprimitives. ThiscontinuumwasevidentintheelicitationresponsesforK’ichee’. ThesemanticcontinuumwasperhapsbestexemplifiedintheK’ichee’an strategiesforpredicatingpropertyandobjectwords.Theresponsesandsubsequent grammaticalityjudgmentsincludedarangeofcertaintywithmorethanacouple “awkward”butgrammaticallyacceptableresponses.Muchoftheuncertainty 52 revolvedaroundtheuseofaree,which—inadditiontoitsotherfunctions—serves essentiallyasacopulaintheseexamples.Generally,thecopulaisnecessaryfor predicatingNouns,butnotforpredicatingAdjectives.However,theresponsesto elicitationindicatethatthedistinctionisnotascategoricalasitseems.(11)shows thepredicationofthreelexemesinK’ichee’. (11) ObjectandpropertypredicationinK’ichee’Mayan(Mondloch,pers.comm.) a. <<ThewomanisGOOD>> utzleixoq utz le ixoq good DEM woman ‘Thewomanisgood.’ b. <<Thealligator’shomeisRIVER>> (aree)ja’ro’chleayiin (aree) ja’ r-o’ch le ayiin (COP) river 3.POSS-home DEM alligator ‘Thealligator’shomeistheriver.’ c. <<ThelazyoneisWOMAN>> areeixoqleq’or aree ixoq le q’or COP woman DEM lazy(one) ‘Thelazyoneisthewoman.’ Theexamplesaboveareorderedsuchthatthelexemebeingpredicatedgoesfrom mostprototypicallyproperty-liketomostprototypicallyobject-like.While(11a) and(11c)fitthegeneralizationforcopulausegivenabove,thecopulaareeis optionalin(11b). Forthemostprototypicalpropertywords,suchasexample(11a)thecopula isnotrequired.TheseelicitationspresentednoproblemfortheK’ichee’speaker. However,forlexemesthatfallsomewhatintermediatetothepropertyandobject prototypes,itbecamemoredifficulttodeterminewhetherthecopulawasnecessary 53 ornot.Movingclosertotheobjectprototype,‘river’in(11b)elicitedagrammatical sentencebothwithandwithoutthecopula;however,thissentencewithoutthe copulawasapproachingungrammaticality.Finally,(11c)showsthatthemost prototypicalobjectwordsrequirethecopulainpredication—itisungrammaticalto leaveitout.Moreover,thissentencewasstillconsideredtobesomewhatawkward evenwhenthecopulawasincluded. Thistypeofgradienceingrammaticalityislikelypresentinsomeforminall thelanguagesusedinthisstudy.Ifoundonlyacouplementionsofitinthe resources,however.Asdiscussedabove,theauthorsoflanguageresourcesare forcedtogeneralizeandcategorizeforthesakeofeconomyandreadability.Asa result,muchofthiskindofvariationisnotrepresentedinthisstudy. 4.4.4. Variationthatisnotcapturedinthestudyduetolistofconcepts Insomecases,thelistoflexical-semanticconceptsthatIchoseforthisstudy resultedintheomissionoflinguisticvariation.Aclearexampleofthiscomesfrom Toqabaqita’slonelyAdjectiveclass.ThereisonlyoneAdjectiveinToqabaqita— ‘small’—anditisnotaconceptincludedinthisstudy.Thiswordcomesbeforea Nounandithasthreeformsthatvaryaccordingtoitsnumber,animacy,and count/massstatus. (12) Toqabaqita’sonlyAdjective(Lichtenberk2008:230) kasi biqu faqekwa small house be.small ‘(very)smallhouse’ 54 Unfortunately,theinflectionalpotentialthatmakesthislexemeuniquein Toqabaqitaisnotrepresentedinmyanalysis. ToqabaqitaalsofeaturestwobasicclassesoftransitiveVerbs(Lichtenberk 2008:51).Inoneclass,onlythird-persondirectobjectsareindexedontheVerb.In thesecondclass,alldirectobjectsareindexed.Thelexemesusedinthisstudy includeonlytransitiveVerbsofthefirstclass,sothisdistinctioninmarkednessis notcapturedinthedataeither. 4.4.5. Precisioninmeaning Anotherchallengeinthecollectionofdataistheproliferationofsensesthata wordcantakeon.Thiswasespeciallyapparentamongactionwordsinmany languages.Mypredictionsformeaningfulconceptualdifferencesamonglexemes includedfineaspectualdistinctions,andtheseareoftentranscendedbythesenses ofasingleword.AcommonexampleistheconceptualtargetWEAR,whichis supposedtorepresentaninactiveactionofpossession.However,inmanylanguages, thisconceptisrepresentedbyawordthatalsomeans‘puton’,whichissurelynot inactive. Thispolysemyisnotinitselfaproblemforthemethodology.Afterall,ifthe wordcanbeusedfortheinactivemeaning,thenitmustbeincludedinthestudy. However,withEnglishusedasameta-language,theprecisemeaningofthewordin thesourcelanguagecangetlostintranslation.Englishdoesnotmakeallpossible semanticandgrammaticaldistinctions,soEnglishtranslationsdonotalwaysconvey thesesubtledistinctionsinthesourcelanguage.IfawordistranslatedtoEnglishas 55 ‘sit’,itmightrefertotheinchoativeactionofsittingdownorthestateofbeingina sittingposition.Tobeclear,thisisnotacriticismofthesourcematerials;itwould beunrealistictoexpectthislevelofdetailinadictionarythatwascreatedwithso manyotherneedsandprioritiesinmind.Thisrealityjustmeansthatsome assumptionsmustbemadealongthewayregardingtheexactmeaningsofcertain words.Onepossiblesolutionistoelicitdatausingimagesorvideos(e.g.,Levinson& Meira2003),butthatwasnotrealisticforthescopeandtimeframeofthisproject. 4.5. Linguisticconsiderations 4.5.1. Morphemecounting Amethodologicalchallengethatcanbetakenforgrantedinanalyzing differencesinstructuralcodingismorphemecounting.Itcanbedifficultto determinewhetheranadditionalmorphemeispresentinsomecases.Asimple examplefromKisiissufficienttoillustratethispoint.Considerthederivational formsof(13). (13) LexicalderivationinKisi(Childs2000) Word: LexicalClass: lùsùlló‘beheavy’ Verb lùsùlló‘weight,heaviness’ Noun lùsùl-ɛ́ í‘heavy’ Adjective ThereisnodifferencebetweentheVerbalandNominalforms,whichfeaturea singleóvowelattheendoftheword.TheAdjectivalform,however,replacesthisó withatwo-vowel-ɛ́ ísuffix.Forthisstudy,Iacceptthemorphologicalanalyses 56 providedbythegrammarauthors,sincetheyarelikelyinformedbytheirexpertise inthelinguisticanalysisofthelanguage. 4.5.2. Choosingamonglexicalcandidates Anothercommonchallengeistheexistenceofmultiplewordsinalanguage foroneconcept.Iwasabletonarrowdownthepossibilitiesinmostcasesby favoringwordswiththehighestfrequency(oftenoneoftheoptionsismarkedas rareorarchaic)andthemostgeneralmeaningforthetargetconcept.Insomecases, morethanonelexemeforaconceptwasretained.Iftheselexemesexhibited differentmarkednesspatterns,thatdiversitywasincludedintheanalysisinthe samewayasthediversityofasinglelexemethatcanbeusedinmultiple constructions.Forexample,KisihasbothanAdjectivekɛ̀ ndɛ̀ meaning‘good’anda Verbnàŋɔ̀ ɔ́meaning‘begood’(Childs2000).Sincetheselexemesfallintodifferent classes,theyexhibitdifferentmarkednesspatterns,andthisdiversityhas consequencesfortheplacementofthisconceptinthemultidimensionalscaling analysis. 4.5.3. Compounds Compoundingandlexicalizationpresentachallengetoanysynchronic analysis,includingthisone.Thisissueistoobroadtoincludeafulldiscussionhere, butthechallengecanbeillustratedwithexamplesfromthedata. InhisgrammarofCreek,JackMartinidentifiesthreedistinctconstructions onthelexicalizationpathwayfromtheparticiplemodifyinganountothefully 57 lexicalizedcompound.Hewritesthat“wordsdescribingcolor,shape,age,orsize commonlycombinewiththenounstheymodifyinCreek,asinmaifa-lást-i‘that blackdog’,butthesearedistinctgrammaticallyfromcompounds”(2011:114).The threeconstructionsareshownin(14a-c). (14) Noun-ParticipleLexicalizationinCreek(Martin2011:125) a. Noun+Participle: ifá lást-i: dog black-DUR ‘blackdog’ b. Noun+ReducedParticiple(adjoined): ifa-lást-i dog-black-(DUR) ‘blackdog’ c. Noun+ReducedParticiple(compounded): fos-cá:t-i bird-red-(DUR) ‘cardinal’ Thekeytothisanalysisissemantic:acompoundtypicallyhasafixedreferencetoa type.ThemostlexicalizedofthesethreeconstructionsinCreekexhibitsthefixed reference—inthisexample‘cardinal’ratherthananyredbird. 4.5.4. Diachrony Diachronyalsopresentsachallengetothekindofsynchronicanalysissought inthisstudyinmuchthesamewayastheothergradientphenomenadescribed above.Withgradualprocessesoflexicalizationandgrammaticalization,thequestion arisesastowhichhistoricalcomponentsofawordarestillrecognizabletothe speaker.InAmericanEnglish,forexample,thewordhappyderiveshistoricallyfrom theLatinhap(‘chance,fortune’)plusthecommonadjectivizingsuffix–y.However,a 58 contemporaryanalysislikelywouldnottreathappyasamulti-morphemic,andmost Englishspeakerswouldnotrecognizethisinternalstructureeither.Thequestionof whatiscognitivelyrealtospeakersisnotalwayseasytodetermine,particularly fromagrammarordictionary. Kanuri’stwoverbclassesareaprimaryexampleofthisdiachronicchallenge. Class2Verbsincludearootnwhichcomesfromalexememeaning‘say,think’,and thesemanticcomponentsoftheseClass2Verbsderivefromnon-Verballexemes. Class1Verbsdonotincludethisroot. (15) VerbalmorphologyinKanuri(Hutchison1981:90-1) a. Class1: búkìn bù-k-ìn eat-1SG-IPFV ‘Ieat’ b. Class2: lěngîn lè-n-k-ìn go-say/think-1SG-IPFV ‘Igo’ ItisnotclearwhetherthemodernspeakersofKanurirecognizetherootnasa distinctmorphemeoraspartofalargerrootwiththesemanticcomponent.The analysisofmarkednessdependsonthisdetail,sinceClass2Verbshaveextra structuralcodingifthisrootisanalyzedasseparate.Forcasessuchasthese,Idefer tothemorphologicalanalysisofthegrammar.Ifthegrammarpresentsthese componentsasseparatemorphemes,thenItreatthemassuch.Althoughgrammar writersmaybebiasedtowardanatleastpartiallyetymologicalanalysisof 59 morphologybecauseitoffersadeeperlevelofexplanation,Iamnotinapositionto determinewhetherthisisactuallythecaseandforwhichanalyses. 4.5.5. Constructionalvariation Languagesoftenpresentmultiplesolutionstothesameproblem.Thisistrue alsoofconstructionalpossibilitiesforagivenlexemeinaparticularpropositional actfunction—thereisalltoooftenmorethanoneconstructionavailable.These possibleconstructionsmaybeequallyavailabletoalllexemesofacertainlexical classorrestrictedtoacertainsubset.Inthesecases,theconstructionalpossibilities affordedaparticularlexemearerelevantforthisstudy,especiallywhenone constructionismoreorlessmarkedthantheother.Theyareincludedwhenthe informationabouttheirapplicabilityisdescribedinsufficientdetail.Themethodfor codingdatainthisstudyallowsaparticularlexemetobeanalyzedashavingmore thanonelevelofmarkedness.Infact,thesecasescanresultinaricheranalysis.Each lexeme/propositionalactfunctioncombinationwithmorethanoneconstructional possibility—onemoremarkedandonelessmarked—exhibitsbothsimilarityand dissimilaritywithalexemethatisalwayslessmarked,andexhibitsbothsimilarity anddissimilaritywithalexemethatisalwaysmoremarked.Thusjusttwolevelsof markednesscanyieldthreedifferentlexicalclasseswhenonelexicalclasscanbe usedinbothconstructions. Insomecases,morethanoneconstructionispossiblebuttherearestrong correlationsbetweentheconstructionandanotherpredictivefactor.Anexampleof thiswaspresentedin(2)forToqabaqita.Higher-animatesubjectsusuallytrigger 60 numberagreementintheSubjectMarker,whileinanimateandlower-animate subjectsarealmostalwaysusedwiththesingularSubjectMarkerregardlessoftheir number(Lichtenberk2008:51).Thisisjustageneralization,andthereare exceptionsforbothgroups.Again,forcaseslikethese,Idefertotheanalysis providedinthegrammar.Thatis,ifthegeneralizationisstrongenoughthatthe grammarwriterisabletoidentifyaboundary,thenIincludethatdistinctioninmy analysis. Someconstructionalalternativesarebasedonotherfactors,suchasthe particularrelationshipofthatlexemetotheconstructionoranotherelementtherein. Forexample,Toqabaqitafeaturesseveralpossibleconstructionsforobjectwordsin modification.Theserepresentrelationshipssuchasgenitive,possession, part/whole,andassociation(16a-c). (16) Noun-NounmodificationconstructionsinToqabaqita(Lichtenberk2008b: 385,379,408) a. Juxtaposition: rua araqi loo work mature.man upward ‘God’swork’ b. Personalsuffix: gwero-na kuukua crest-3.PERS chicken ‘a/thechicken’screst’ c. Associativesuffix: biqu-i dudualinga house-ASSOC bee ‘bee’snest/beehive’ 61 Itisnotcleariftheserelationshipsarerestrictedtoparticularlexemesorsubclasses ofobjectwords.Thisstudydoesnotaimtocapturemarkednessdifferencesamong thesekindsofmodifyingrelationships. 4.5.6. Semanticshift AclassicexampleofsemanticshiftinmydatacanbefoundinQuechua. Whenpropertywordsareusedinreference,theycanappearwithoutanystructural coding.However,thereisarecognizableshiftinmeaning. (17) ObjectsandpropertiesinreferenceinQuechua(Weber1989:35-6) a. rumi-ta rikaa stone-ACC I.see ‘Iseea/thestone.’ b. hatun-ta rikaa big-ACC I.see ‘Iseea/thebig(one).’ Inthesecondexample,theresultingconstructiondoesnotrefertothepropertyof ‘bigness’,butrathersomeoneorsomethingthatexhibitsthatproperty.Although thispropertywordappearswithoutanystructuralcoding,ithastakenonanobject meaninginthisconstruction(Croft1991:72-74,2001:70-5).Thisparticular semanticshiftforpropertywordsinmodificationisverycommoncrosslinguistically.Anyinstanceofsemanticshiftthatchangesthebroadsemanticclassof alexeme(e.g.,frompropertytoobject)isconsideredtoomuchsemanticshiftinthis study. Anotherexampleofunacceptablesemanticshiftisfoundforactionsin reference.Someactionconcepts,suchasCOUGHandFIGHT,appearasNounsin 62 severallanguages.AsNouns,however,theyrefertoaproductoftheeventrather thantheeventitself(Croft,pers.comm.).ForFIGHT—likeDANCE—theproductisthe structureoftheaction.Foremissionevents,theoftenephemeralproductiswhatis emitted:sound,light,orsubstance.Theseproductsallrepresentnon-prototypical objectsratherthanderivedactions. Forsomelanguages,specificderivationalmorphemesencodethe relationshipbetweenthestemandthederivedlexeme.Thisisatleastgenerallytrue forthenominalizationofQuechua’sVerbs.Thesuffix–qisan“Agentive Substantivizer”,bywhichtheresultingNounreferstotheagentoftheaction denotedbythesourceVerb(Weber1989:53).Similarly,thesuffix-nacreatesa NounthatreferstothetoolusedtodotheactiondenotedbythesourceVerb,such asthederivationof‘broom’fromtheVerbmeaning‘sweep’(50-1).Neitherofthese arerelevantforthisstudy,sincetheyshiftthereferentfromanactiontoanobject.A thirdQuechuanominalizeristhesuffix–y,an“InfinitiveSubstantivizer”(51-3). WhenattachedtosomeVerbs,theresultingNounappearstomaintainitsaction meaning.However,otherexamplesindicateasignificantsemanticshiftonparwith theotherQuechuanominalizersdiscussedabove.Stillothersareglossedinsucha waytobesomewhatambiguousastothedegreeofsemanticshift. (18) InfinitiveSubstantivizingsuffixinQuechua(Weber1989:51-3) a. miku-y eat-INF ‘food’ b. kanan papel-niki-kuna tinku-chi-y-chaw lloqshi-nki alli now paper-2.POSS-PL equal-CAUS-INF-LOC come.out-2 good ‘Nowin(thecircumstanceof)seeingifyourpapermeasuresup,youwill comeoutgood…’ 63 c. aqcha rutu-y ka-n hair cut-INF be-3 ‘Thereishair-cuttinggoingon.’ Becauseitisimpossibletodeterminefromtheavailabledatahowthissuffix interactswitheachlexemeinthisstudy,thisinformationisleftoutoftheanalysis. Kanurifeaturesasimilarsemanticambiguityforpropertywordsin reference: Thewordcìmêforexamplemaybeusedasamodifiermeaning‘red’,orasa nounmeaning‘theredone’,oreventomean‘redness’,thoughforthelatter meaningthereisalsothealternativederivednə̀ m-cìmê‘redness’formedthrough theaffixationoftheabstractnominalprefix…Thewordkúràmeans‘big’, ‘important’asamodifier,andeither‘boss’,‘leader’,‘head’or‘thebigone’,‘the importantone’,whenusedasanindependenthead…Anyofthewordsthathave traditionallybeentranslatedasandreferredtoasAdjectivesinKanurican functionasnounphraseheads,referringbacktoananaphoricallyunderstood nounphraseantecedent.(Hutchison1981:36) Thesemanticallyshiftedmeaningfrompropertytoobjectisgeneralizabletoall membersoftheKanuriAdjectiveclass.However,itisunclearifallofthemcan renderapropertymeaninginreference,asisdescribedfor‘red’.Sincethelatter meaningisthetargetofthisstudy,somefactsabouthowKanuri’sAdjectivesare usedinreferencearesimplymissingfromtheanalysis. Theexamplespresentedaboveshouldmakeitclearthataprimarychallenge totheinclusionofderivationalmorphologyinmyanalysisisthevariabilityof semanticeffectofsomederivationalmorphemes.Combinedwithdifferentlexemes, thesamemorphemecanoftenderiveresultswithcriticallydifferentlevelsof semanticshift.Thisvariabilitymakesitverydifficultforthegrammarwriterto makeusefulgeneralizationswithoutcompromisingsomeofthedetailsthatareso crucialforthiskindofstudy. 64 Thistopicalsohasimplicationsfortheoverallprototypestructuresofparts ofspeech.Iwilldemonstrateinlatersectionshowthefullrangeofmarkednessdata supportsmanyofmyhypothesesaboutwhichsemanticcriteriaaremostinfluential inshapingtheprototypestructures.Someofthesearesemanticfeaturesthatexert theirinfluenceattheheartsoftheprototypes.Forexample,theanimacyhierarchy mostcommonlycreatesdistinctionsininflectionalpotentialwithintheprototypical combinationofobjectsinreference.Thesederivationalconstructions,however,are primarilyaffectingstructuralcodinginnon-prototypicalcombinationsofsemantic classandpropositionalactfunction.Interestingly,thesamesemanticcriteriathat I’vepredictedtoberelevantattheprototypesandoverallmaybedifferentfromthe criteriamostrelevanttominordistinctionsinthe“noman’slands”wherethese derivationalconstructionsareatwork.Thisisbecausesemanticshiftbecomesthe dominatingfactorinthesenon-prototypicalconstructions. Whenthesemanticclassdoesnot“match”thepropositionalactfunction, thereisatug-of-warbetweenthesemanticcontributionofthelexemeandthe semanticcontributionoftheconstruction.Thisiswhy,forexample,propertywords inreferencesooftensemanticallyshifttorefertoapersonorthingthatexhibitsthat property.Thenewlyformedobjectwordfitscomfortablyinreference,asobjects generallydo.Thenaturalfitandfrequencyofthissemanticallyshiftedderivational constructionmaycoercealanguageintoderivingadifferentconstructionforthe less-commonlyusedreferenceofthepropertyitself,andthatalternative constructionmaybemarkedrelativetothederivationinvolvingsemanticshift.For thoselanguagesinwhichthederivationalsemanticsareinconsistentandlexically 65 specific,markednesspatternscouldboildowntohowpredictableasemanticshiftis forthatlexicalconcept.Asdiscussedabove,someactionconceptsrefertoanevent ofemissionwithanidentifiableproduct.Theproductmaybeanobvioustargetfor semanticshiftwhenthatlexemeisusedinreference,whileotheractionconcepts withoutsuchanobvioustargetmayresistasimilarsemanticshift.Thiswouldresult indifferencesinmarkednessfortheseactionsinreference.Thesemantic characteristicsthatdeterminewhetheranactionhasanobvioustargetforsemantic shiftmaybedifferentfromtheaspectualcharacteristicsthatwerehypothesizedto berelevantforthisstudy.Thiscouldbeinvestigatedwithmoredetailedlanguage data,particularlyforthoselanguagesinwhichonlysomeactionwordsundergo semanticshiftwhenusedinreference. 4.5.7. Apparentarbitrariness Theexamplesfromthedataandthediscussionofchallengestothe methodologyhaveillustratedthatmarkednessdifferencesariseoutofthe confluenceofseveralfactors.Historicaldevelopmentsinteractwithfrequency effectsandprocessesofmorphosyntacticreanalysisandphonologicalfusion.A comprehensiveunderstandingofthesefactorswouldrequireconsiderationofthe uniquecircumstancesineachlanguageandintheevolutionofeachconstruction. Forexample,CreekVerbs(thisclassincludesAdjectives)indexthenumberoftheir subject(intransitive)orobject(transitive)inthreedistinctways(Martin2011:197). AsmallsetofcommonVerbs(e.g.,‘sit’,‘come’,‘die’,‘break’)achievethisthrough suppletion.Alternatively,reduplicationaccomplishesthisinflectionformanystative 66 Verbroots(suchasAdjectives—e.g.,‘white’,‘soft’).StillanothersubsetofVerbs(e.g., ‘red’)utilizeasuffix.Thesedifferentstrategiesobviouslyreflecttheinfluencesof frequencyandstativity,butthefullstoryismuchmorecomplex.Howdoes‘red’end upinadifferentclassthan‘white’?Atleastonthesurface,frequencyandstativitydo notappeartoanswerthisquestion. InKanuri,seeminglyminordifferencesinthephonologicalformofaVerb turnouttohaveamajorimpactonmarkedness(Hutchison1981:157).Verbal Nouns(basicallyinfinitives)canbederivedfromClass1KanuriVerbsintwo differentways.ForCVrootsfeaturinganyvowelexcept/i/,atonechange accomplishesthisderivation(e.g.,tá->ta).Incontrast,thesuffix–oisaddedto derivetheVerbalNounforalmostallCVrootswith/i/,CVCroots,andpolysyllabic roots(e.g.,dí->di-o,lad->lad-o).Thissuffixisadditionalstructuralcodinginmy analysis,andtheresultingVerbalNounformismarkedrelativetothoseVerbal Nounsderivedfromonlyachangeintone.ThevowelofaKanuriCVrootisnottruly random,sinceitcomesfromaparticularsetofhistoricalprocessesatworksinceits inception.Yetitseemstobeafairlytrivialcharacteristicoftherootfromwhicha significantmarkednessdistinctionhasarisen. Theseexamplesareillustrativeofthemyriadoffactorsmotivatingthe prototypestructuresofpartsofspeechandthecomplexrelationshipsamongthese factors.Thatarecognizablyconstrainedpatternemergesfromthesecomplexitiesis atestamenttothestrengthanduniversalityofthefunctionalpressuresmotivating partsofspeech. 67 5. Resultsanddiscussion Themultidimensionalscalinganalysiswassuccessfulinrepresentingmostof thevariationinthedatainonlytwoorthreedimensions. Table10.Fitnessstatisticsinone,two,andthreedimensionsfortheMDSanalysisof partsofspeech Dimensions 1 2 3 Classification 0.94030 0.98679 0.99212 APRE 0.79240 0.95406 0.97261 Asdiscussedabove,thebestmodelyieldsahighdegreeoffit,andforwhicheach additionaldimensionoffersonlysmallimprovementstothefit.Forthepartsof speechdata,atwo-dimensionalmodelclearlyfitsthisdescription.Theclassification andespeciallytheaggregateproportionalreductionoferrorshowasignificant improvementfromonedimensiontotwo.However,fromtwotothreedimensions, thesenumbersonlyshowsmallimprovement.Thetwo-dimensionalmodel demonstratesanexcellentfitwhileremainingveryinformative—itcharacterizesa largedatasetwithverylowdimensionality. AcommonresultofanMDSanalysisintwodimensionsisahorseshoeshape (BorgandGroenen1997,citedinCroft&Poole2008:17),andthisshapewasfound forthepartsofspeechdata.Thedataarearrangedlinearly,andthatlineisbentinto ahorseshoeshapeallowingstraightcuttinglinestoisolatesmallerstringsofdata thatfallalongtheline.Thelinearnatureofthisdatacorrespondstothesemantic continuumfromobjectstopropertiestoactions.Ifsomegrammaticalfunctions applytoasubsetofthedatapoints(lexemes)inthemiddleofthelinear arrangement—suchasanAdjectivecategoryinalanguagethatincludesproperties 68 butexcludesobjectsandactions—thelinemustbecurvedtoallowastraightcutting linetoseparatethosepointsfromtherest.Figure3showsthatthehorseshoeshape pointsupward;i.e.,theopenendofthehorseshoeisatthetopofthefigure.For moreevenlydistributeddata,pointswillappearallalongthehorseshoeshapewith theendsdrawingsomewhatneartoeachother.Becausethepartsofspeechdataare dominatedbythreeclusterscorrespondingtoNouns,Adjectives,andVerbs,the endsofthehorseshoeshapedonotcometogetherinthisway.Anopenendstill facesupward,butthepointsformmoreofanopen,“U”shape. Thereisanotherimportantcharacteristicofthishorseshoeshapethatwillbe relevantinthediscussionbelow.Althoughthedatapointsaregenerallyalignedin theshapeofthehorseshoe,somepointsendupclosertotheinsideofthehorseshoe andsomeendupalongtheouteredgeofthehorseshoe.Thisisnotanaccident,but ratheranemergentcharacteristicofthespatialmodel.Thepointsalongtheoutside ofthehorseshoecanmoreeasilybeisolatedfromtherestbyacuttingline,while pointsalongtheinsideofthehorseshoecannot.Therefore,ifgrammatical distinctionsseparateoneparticularconceptualtargetfromtherest,thatpointis likelytoshowupalongtheoutsideofthehorseshoe.Conceptualtargetsthatare neverorless-oftenindividuatedinthedatawilllikelyendupalongtheinsideofthe horseshoe.Inthisway,thepositionofapointalongtheinsideoroutsideofthe horseshoeistheoreticallysignificant,notjustitspositionalongthecurvedlinefrom oneendofthehorseshoetotheother. 69 Figure3.MDSanalysisofpartsofspeechwithpointsonly 70 5.1. Distributionoftheprototypes TheprototypestructuresofpartsofspeechcorrespondingtoNouns, Adjectives,andVerbsareimmediatelyclearinthespatialmodel.Before investigatingeachoftheseinmoredetail,Ifirstpresentsomeobservationsthatmay seembasicbutneverthelesshavetheoreticalimportance. Figure4.MDSanalysisofpartsofspeechwithcuttinglinesonly 71 Figure5.MDSanalysisofpartsofspeechwithconceptlabelsonly Therearesomepointsthataredirectlyoverlapping,meaningtherewereno distinctionsamongtheminthedata.Yetmanyofthedatapointsaredifferentiated intheanalysis.Whyisthisimportant?Firstofall,thereweremanyconceptual targetsevenwithineachbroadsemanticclass.Eachofthesebroadclasses—objects, properties,andactions—includedatleast16targets.Someoftheconceptualtargets fallintothesamesubclass,suchastheageconceptsYOUNGandOLD.Yetalmostallof theseconceptualtargetsweredifferentiatedfromeachotherbyamarkedness 72 distinctioninatleastoneoftheelevenlanguages.Languagesexhibitconsiderable variationregardingwheretheymakemeaningful,grammaticaldistinctionsalong theobjects-properties-actionssemanticcontinuum. Despitethedifferentiationofnearlyeverydatapoint,thebroadclassification oftheseconceptsintoobjects,properties,andactionsissupportedinthespatial model.Therearegreaterdissimilaritiesacrossthesebroadclassesthanwithineach one.Everylanguageinthestudymakessignificantmarkednessdistinctions betweenmostobjectsandmostproperties,andbetweenmostpropertiesandmost actions.Languagesmakesomedifferentdecisionsintheirclassificationof conceptualtargetsthatareclosetotheboundariesbetweenthesebroadsemantic classes,buttheyallmakeatleastonemajordistinctionnearthoseboundaries.In fact,thisistheprimaryreasonforanalyzingeachofthethreeclustersasseparate prototypes. Inowturntotheprototypesthemselves.First,strongnounandverb prototypestructurescanbeobservedintheupperrightandupperleftregionsof theanalysis,respectively.Asdiscussedinsection(2.6.2.),thespatialmodelisbased ondissimilaritydata,soprototypeclustersareanindirectresultofgreaterand lesserlevelsofdissimilarityamongpoints.Thediscussionhereof“stronger” prototypesreferstodatapointclusterswhichexhibitlessdissimilarity,resultingin pointsthatareclosertogetherintheanalysis.Inadditiontothenounandverb prototypes,aweakeradjectiveprototypeisobservableinthelower,middleregion oftheanalysis.Thesedatapointsrepresentpropertyconcepts,andwhilethey 73 exhibitmoredissimilarityamongthemthanisfoundintheotherprototypes,they maintainsomeamountofcohesionaswell.TheseclustersareillustratedinFigure6. Figure6.MDSanalysisofpartsofspeechwithpointsandprototypes Verb Prototype Noun Prototype Adjective Prototype Inthenextsections,Iturntoadiscussionoftheinternalstructureofeach prototype.Thearrangementofpointsinthespatialmodelwillbeevaluatedto determinewhethertheysubstantiatemypredictionsregardingtherelevant semanticcharacteristicsforprototypicality. 74 5.2. Nounprototype Thenounprototypeisprobablythemoststraightforwardinboththespatial arrangementofitsdatapointsandthesemanticmotivationforthisarrangement. Figure7offersacloserlookatthedatapoints(representedbytheirlabels)that constitutethenounprototype.Allthesepointsrepresentobjectconcepts,andthey nearlyformastraightlineinasteep,positive-slopingdiagonal.Recallthatacurved lineisnecessaryforcuttinglinestoseparatemedialsegmentsfromtherest.The objectconceptsaregenerallyalignedonastraightlinebecausethereisoneprimary functionalmotivationthatdominatestheirarrangement:animacy. Animacywasresponsibleforseveralmarkednessdistinctionsacrossmore thanonelanguageinthisstudy.Theseincludedhuman/non-human,higheranimate/lower-animate,andanimate/inanimatedistinctions.Asaresult,the uppermostpointsinthisclusterofthespatialmodelaretheobjectconceptsthatare mostanimate.Allfourhumanconceptsarefoundatthisextremity,andtheseare followedbynon-humananimates—firstDOGandthenBIRD.WOMANwasalso differentiatedfromBOYeventhoughtheyarebothhumananimateconcepts.For example,theEnglishablautpluralformwomenislessmarkedthanthesuffixed pluralformboy-s. Atthelowerendoftheobjectscluster,STONEandWATERarestretchedoutin thedirectionoftheadjectiveprototype.LexemesforSTONEpatternedwithproperty wordsinseverallanguages,whichexplainsitsspatialorientation.WATERisunableto inflectfornumberinKisiduetoitsstatusasamassnoun,distinguishingitfrom 75 otherobjectconceptsinthisanalysis.Thissuggeststhatunboundedobjectsare mostdistantfromthenounprototypeamongobjectconcepts. Figure7.Nounprototype Betweenthesetwoconceptsonthelowerendandtheanimatesontheupper end,asignificantnumberofconceptslandedinthesamespotinthespatialmodel. ThisgroupincludesTREE,SEED,HAT,BED,ARM,FACE,RIVER,andHOUSE.Nomarkedness distinctionswerefoundtodisambiguatethesetermsinmyanalysis.The explanationmaybefoundinthesemanticfeaturesthatwereusedtosubclassify 76 theseobjectconceptsinthefirstplace.Theseconceptsrepresentseveralsubclasses: plants(andplantproducts),artifacts,bodyparts,andplaces.Theyarenotasreadily rankedintermsofanimacy.Bodyparts(includespartsofhumans,animals,and plants)areindirectlybasedonanimacybytheobjects’relationshipswithhumansor otheranimates.Placesaresubclassifiedseparatelyfromartifactsbasedon magnitude(amongotherthings),especiallyrelativetohowhumansexperience them.Itispossiblethatdistinctionsinmarkednesswouldbefoundtodistinguish thesesubclassesandtheirconceptsifmorelanguageswereaddedtothestudy.Even so,thisstudysuggeststhatplantsandbodypartsaretreatedlikeinanimateswith regardtopartsofspeech.Distinctionsamongtheseconceptsmightbereservedto lowerlevelgrammaticalstructures.Forexample,alienableandinalienable possessionrelationshipsareencodeddifferentlyinmanylanguages,whichcould disambiguatebodypartsfromartifacts.Thisgrammaticaldistinctionisless obviouslyrelatedtotheoccurrenceoftheselexemesinaparticularpropositional actfunction;rather,ithasimplicationsforhowthatobjectconcepttakespartina possessor-possesseerelationship. Insummary,theinfluenceofanimacyisrobustinthenounprototypeinthis analysis.Unliketheotherprototypeclustersdiscussedbelow,theobjectconcepts arenearlyarrangedinastraightlinereflectingthisimplicationalhierarchy. Furthermore,someknownconceptualdistinctionssuchasalienable/inalienable andplant/inanimatemayonlyberelevantforgrammaticalstructuresunrelatedor indirectlyrelatedtopartsofspeech. 77 5.3. Adjectiveprototype Themostobviouscharacteristicoftheadjectiveprototypeisthatitisless cohesivethanitsnounandverbcounterparts.Thesepropertyconceptsforma relativelylooseclusterthatcoversalargerareaofthespatialmodel.Furthermore, thisclusterdoesnotformedthecurvedlinethatwemightexpectatthebottomofa horseshoearrangement.Instead,thepointsarespreadsignificantlytotheinside andtheoutsideofthehorseshoe.Figure8givesacloserlookatthissectionofthe spatialmodel.Afewoverlappingconceptsneedtobeidentified,sincetheyare difficulttoreadevenintheenlargedimage.Theoverlappingconceptsintheupper rightcornerofthissectionareMALEandFEMALE.Inthecenterofthesection,just aboveSHORT,aretheconceptsGOODandOLD.Finally,YOUNG,FLAT,andBIGaretheat leastpartiallyoverlappingconceptsinthelowerrightsection. Theanalysisofpropertyconceptsintheliteratureasacontinuumfrom object-liketoaction-likeispartiallysupportedbytheanalysis.Specifically,those propertyconceptsthathavebeenplacedontheextremesofthiscontinuumshowup thiswayintheanalysis.ThegenderconceptsMALEandFEMALEwereexpectedtobe themostobject-likepropertyconceptsbasedonthisliterature,andtheyappearin theupperrightcornerofthepropertyconceptcluster,closesttothenounprototype. Aspredicted,thecolorconceptsWHITEandREDappearintheupperrightcorneras well,justbelowandtotheleftofthegenderconcepts.Ontheaction-likesideofthe continuum,humanpropensitiesarefoundintheupperleftcornerofthiscluster, closesttotheverbprototype.Justalittleclosertothecenterofthecluster,physical 78 propertyconceptsSOFTandHEAVYarerightbehind.Theseobservationsinthespatial modelreinforcetheclaimsintheliterature. Figure8.Adjectiveprototype Theeightpropertyconceptsthatareinthemiddleofthecontinuum— neitherveryobject-likeoraction-like—arenotarrangedsoneatlyinthespatial model.Someoftheseconceptsareclusteredveryclosetoeachother,andtheredoes notseemtobeanobviousstructurefrom“nouny”to“verby”withinthem.Thismay 79 resultfromthelimiteddatasetusedinthisstudy.Datafromadditionallanguages wouldsurelyteaseoutthedetailsattheheartofthisprototypestructure.Acouple ofobservationswillhavetosufficeforthispaper.First,ROUNDappearsinthelower leftcornerofthecluster,withasignificantamountofseparationfromtherestofthe propertyconcepts.Itisnotclearwhatmotivatesthisposition,orwhetherthe inclusionofadditionallanguagedatawouldperpetuateitsoutlyingpositioninthe analysis.Second,BIGappearsinalow,relativelycenterpositionintheproperties cluster,andthereforeontheoutsideofthehorseshoeshape.Thispositionreflects thestatusofBIG(andothercommonconceptsrepresentingdimension,suchas LITTLE)asanextremelyprototypicalpropertyconceptwhichenjoysunique inflectionalcapabilitiesinmodificationinsomelanguages. WhatarethesemanticmotivationsbehindtheprototypestructurethatI havejustdescribedforpropertyconcepts?Althoughtheliteratureidentifies“nouny” and“verby”propertyconcepts,thesemanticmotivationsforthesecharacteristics arenotalwaysexplicated.Basedonthemarkednessdifferencesobservedinthis study,Iwilldiscussthesemanticmotivationsthatseemtobeinfluencingthe structureoftheadjectiveprototypeinthespatialmodel. TheconceptsMALEandFEMALEappearintheupperrightcornerofthe adjectiveprototype,closesttothenounprototype.Inseverallanguagesofthisstudy, theseconceptsareexpressedwithlexemesclassifiedasNouns.Asaresult,these lexemesareunabletoparticipateinAdjectivalinflections,suchascomparativesand superlatives.Thismayreflectaconstrualoftheseconceptsaslackinggradability, andfurtherevidencecomesfromlanguageswhichencodetheseconceptsas 80 Adjectives.InEnglish,maleandfemalecannottakecomparativeandsuperlative suffixeslikemanyotherpropertyconcepts;instead,theyinflectperiphrastically withmore/most.SimilarlyinK’ichee’,theseconceptscannotinflectforcomparative likeotherpropertyconcepts,despitebeingclassifiedasAdjectivesbasedonother formalcriteria. Table11.Semanticpropertiesofprototypicalpartsofspeech(Croft2001:87) Relationality Stativity Transitoriness Gradability Objects nonrelational state permanent nongradable Properties relational state permanent gradable Actions relational process transitory nongradable Onthe“verby”sideofthepropertyconceptcontinuum,thesemantic primitivesthatwereclaimedtodistinguishpropertiesfromactionsaregradability, stativity,andtransitoriness.Gradabilitydoesnotseemtoplaymuchofarolehere. Infact,someoftheconceptsthatlandedintheverbprototypeclustercanbe construedasgradable,suchasWANTandLOVE.Incontrast,theroleofstativityand transitorinesscanbeseeninthemost“verby”propertyconcepts:thoseofhuman propensity.Firstofall,HAPPYandANGRYareknownthroughhumanexperiencetobe transitory.AtleastinEnglish,theycanrefertoanoverallpersonalitycharacteristic, butmoreprototypicallytheyevokeatemporaryattitudeordemeanor.This transitorinessofhumanpropensitiesistiedupwiththeirconstrualasaprocess, sincehumanshavetotransitionintoandbackoutofaparticulartemperament.In fact,thevariouspossibleconstrualsofanexperienceofhumanangerpresenta challengetothisstudy.Alexemerecruitedtorepresenttheexperiencemighttarget theresultingstateortheinchoativeprocess,forexample.Itispreciselythese 81 semanticcharacteristicsinherenttotheexperienceofhumanpropensitythatleadto itsoccasionally“verby”morphosyntactictreatment. 5.4. Verbprototype Theactionconceptsformarelativelytightclusterinthespatialmodel, althoughnotquiteastightasthenounprototype.Also,asisfoundintheadjective prototype,theactionsconceptsnotonlyfollowthecurvedlineofthehorseshoebut exhibitvariationtotheinsideandoutsideaswell.Thisisindicativeofamore complexsetofsemanticmotivationsbehindthemorphosyntacticrealizationof theseconcepts.Inthelowerleft,theoverlappingconceptsareSITandWEAR.Inthe lowerright,SEE/LOOKAT,LOVE,andWANTarebunchedtogether.Finally,GIVE,COUGH, andEATaretheoverlappingconceptsnearthecenterofFigure9. Asdiscussedin2.2.3,aspectualdistinctionswereexpectedtoplayarolein theprototypicalityofactionconcepts.Thishypothesiswasconfirmedwithregardto oneparticulardistinction:stativeconceptsversusdynamicconcepts.Twoparticipantstates(KNOW,WANT,LOVE)andinactiveactions(SEE/LOOKAT,SIT,WEAR)are stativeconcepts,andtheseareclearlyseparatedfromtherestoftheactionconcepts inthespatialmodel.Asexpected,theyarefoundinthelowerandlowerrightareas oftheverbcluster,closertotheadjectiveprototypethantherestoftheaction concepts.Asdiscussedinsomeexamplesabove,thestative/dynamicdistinctionwas foundtobesignificantformarkednesspatternsinsomeofthelanguagesinthis study,andtheresultsareapparentinthemodel. 82 Figure9.Verbprototype Withinthissubgroupofstativeconcepts,however,thedistinctionbetween two-participantstatesandinactiveactionsisnotclearinthespatialmodel.Inactive actionsSITandWEARarefoundtogetherinthelowestpositionintheverbcluster, butSEE/LOOKAT—alsoaninactiveaction—isbunchedupwiththetwo-participant states.TheseparationofSITandWEARfromtheotherstativeconceptsmayreflect theunintentionalinclusionofmoreactivemeaningsinsomeofthelexemeschosen 83 torepresenttheseconcepts.Forsomelanguages,thesamelexemecanmean‘sit’ and‘sitdown’or‘wear’and‘puton’.Thesealternativemeanings—oftendifficultto disentanglefromthestativemeanings—mightbemotivatingtheselexemesto patternmorphosyntacticallyinsomewayswithdynamicactionconcepts. Theremainingactionconceptsarespreadfromthecenterandleftcenter sectionsoftheclustertowardtheupperright.Thisgenerallyfollowsthecurvedline oftheexpectedhorseshoeshapeendingatCOOK.Thesemanticmotivationforthe arrangementofthesedynamicactionconceptsinthemodelislessclear.Durative processes,cyclicachievements,anddirectedachievementsareallintermingled togetherinthispartoftheverbprototype. Thereis,however,atendencysuggestiveofadurative/punctualdistinction.7 Durativeconceptstendtobefoundattheupperandupperrightportionsoftheverb prototype.ThisgroupingmightincludeCOOK,WAVE,DIE,FIGHT,andBUILD.WAVEwas chosenforthisstudytorepresentacyclicachievement,butiterativelyconstrued,it couldtakeonadurativesense.DIEwouldbeaclearexceptiontothedurativetheme. Punctualconcepts,ontheotherhand,arefoundclusteredinthecenterandleft centeroftheprototype.TheseincludeCOME,HIT,BREAK,GIVE,COUGH,andEAT.COMEwas selectedtorepresentadurativeprocess,butanalternativeconstrualforthis concept—‘arrive’—couldexplainitsplaceamongpunctualconcepts.However,itis difficulttoconceiveofastrictlypunctualconstrualofEAT,sothisconceptmustbe consideredanexceptioninthisgroup. 7IamthankfultoBillCroftforbringingthistomyattentioninourdiscussionofthe resultsoftheMDSanalysis. 84 Ifweacceptthedurative/punctualdistinctionsuggestedinthemodel,why arethedurativeconceptsfoundattheendofthehorseshoe(upperrightportionof theactionconceptscluster)whereverbprototypicalityishighest?Ahypothesisof thisstudywasthatpunctualactions—representedbycyclicachievementsand directedachievements—wouldbemoreprototypicalamongactionsthandurative ones.Theanswermaylieinanunexpectedrelationshipbetweenachievementsand states.Fromaperspectiveofboundednessorcompletion,thesetwoaspectual classesappeartobeverydifferent.However,thereisevidencethattheyareunited aspotentialconstrualsofadirectedaspectualcontour(Croft2012:165-71).This contourcorrespondstooneofBybeeetal.’s(1994)familiesofgrammaticaltenseaspectcategorieswhichincludesperfectandperfectivesenses.Synchronicallyand diachronically,verbscanalternatebetweenanachievementconstrualand (resulting)stateconstrual.(Alternatively,durativeconceptsareconstrualsofan undirectedaspectualcontour,correspondingtoBybeeetal.’sotherfamilyof grammaticaltense-aspectcategorieswhichincludesimperfective,progressive, present,andhabitualsenses.)Thissurprisingrelationshipmaybemotivatingthe contiguityofpunctualandstativeconceptsinthepartsofspeechmodel. Thespatialmodelhasprovidedvariousinsightsintothenatureoftheverb prototype,butitdoesnothavetoexplaineveryaspectofthedata.Severalofthe languagesinthisstudyfeatureimportantmarkednessdistinctionsamongaction concepts—particularlywithregardtohowtheyarepredicated—thatdonotseemto resultfromaspectualdistinctions.Forsomelanguagesthesedistinctionsarethe resultofverbclasses,forotherlanguagesitisduetotheexistenceofsemantically 85 meaningfulauxiliaryverbs.Itisalsopossiblethatindividuallexicalfrequency manifestsitselfinmorphosyntacticmarkednessenoughtodisrupttheinfluenceof aspectualprototypicality.Itisinterestingthatthisdoesnotalsohappenwithin objectconcepts.Forexample,noteverylexemerepresentingahumanconceptis frequent,yetincertainlanguagesthemorphosyntactic“advantages”suchas inflectionalpotentialareappliedtoalllexemesrepresentinghumanconcepts.What differentiatestheactionconceptsfromtheobjectconceptsinthisregard?Itmaybe thattheconceptualunityofsubclassesintheanimacyhierarchyisgreaterthanthat ofaspectualsubclasses.Alternatively,thegrammaticalprocessesbywhich markednesspatternsevolveinobjectsmaybedifferentthatthoseforactions,soit maybeonlyindirectlyrelatedtothesemanticcharacteristicsthatmotivatethese subclasses. 5.5. Interpretingthedimensions Partsofspeechrepresentoneofthemostbasicandpervasivelevelsof grammaticalorganization,anditwouldbeextremelysurprisingifonlytwoorthree functionalorsemanticfactorswereshowntoexplainnearlyallofthedata.Rather, thesedimensionsrepresentdifferentsemanticmotivationsindifferentareasofthe spatialmodel.Forexample,withinthenounprototypecluster,onecouldarguethat ananimacydimensionrunsalongasteep,positive-slopingline;highlyanimate conceptsarehigherandfurthertotheright,whileinanimateconceptsarelowerand furthertotheleft.However,ananimacyinterpretationofthisdimensionis 86 inappropriateforotherareasofthemodelwhereothersemanticfactorsappearto bedrivingthedistributionofconcepts. Themultidimensionalscalinganalysisconfirmsthehypothesisofthisstudy thatatleastseveralsemanticfactorsarerelevantforthemorphosyntactic realizationofobjects,properties,andactionsinthreepropositionalactfunctions. Table12showstheprimarysemanticfactorsbasedonwheretheireffectisfoundin thesemanticcontinuumfromobjectstopropertiestoactions.Mostofthese semanticprimitivesarefamiliarfromCroft(2001:87,seealso1991),andtheywere introducedinTable1.However,theeffectofanimacyinthisanalysisisstrong enoughtoincludeitwiththeothersemanticfactors. Table12.Semanticpropertiesmotivatingtheprototypestructuresofpartsofspeech ACTIONSPROPERTIESOBJECTS dynamic stative transitory permanent nongradable gradable relational inanimate nongradable non-gradable nonrelational animate human WhileTable12impliesthatallofthesesemanticprimitivesareactiveacross theentirecontinuumfromobjectstopropertiestoactions,thismaynotbethecase. Stativityandtransitorinessarecomponentsofthelargerconceptoftime-stability, andtheyappeartoberelevantacrossthecontinuum.Similarly,relationality motivatesadistinctionbetweenobjectsandproperties,soitseffectsarenot confinedtoasingleprototype.However,animacymayberelevantonlyamong nonrelationalconcepts,effectivelylimitingitsdomaintothenounprototype.Itmay 87 beappropriatetosaythatanimacycontributestotheinternalstructureofthe prototype,butnottothe(non-discrete)boundarybetweenprototypes.Itisless clear,butgradabilitymaybesimilarlylimitedinitsscope.Itseffectsareprimarily foundinthemiddleofthecontinuum—thatis,amongpropertyconcepts—andless sooneitherend. Theanalysispresentedhereshowsthathumanscanindeedreducethe complexityoftheworldintojustafewdimensions,butthatthosedimensionsmay actuallyrepresentvariousfunctionalorsemanticprinciplessimultaneously. 5.6. Futureresearch Themostobviousextensionofthisstudywouldbeitsapplicationtomore languages.Theelevenlanguagesincludedinthisstudyarejustenoughtoillustrate theprototypestructures.ThiscanbeseeninFigure10,whichshowsspatial analysesusingdatafromthree,six,nine,andelevenlanguages.Ittakesclosetoa dozentoteaseapartmostoftheconceptsinthespatialmodel.Doublingortripling thesamplesizewouldgoalongwayinfurtherelucidatingdissimilaritiesandtheir semanticmotivations. Additionally,futureresearchcanimproveonthelistofconceptsincludedin suchastudy.Ofcourse,withmorefine-grainedconceptualdistinctions,amore informativemodelispossible.However,economicalconsiderationslimitthe numberofconceptsthatcanbeincluded,sofutureworkmightinsteadreplace uninformativeconceptualdistinctionswithonesthatarepredictedtoberelevant basedontheresultshere. 88 Figure10.MDSanalysisofpartsofspeechforthree,six,nine,andelevenlanguages (clockwisefromtopleft) 5.7. Experience,conceptualstructure,andlinguisticform Thisstudyisaninvestigationintohowthesameconceptualideamaybe encodedgrammaticallyindifferentwaysfromonelanguagetothenext.Thisisthe sourceofvariationbetweenthepartsofspeechprototypes.Thereis,however, anothersourceofvariationinpartsofspeechamonglanguages.Thesamereal 89 worldtarget—whetheranobject,property,oraction—canbeconstrueddifferently acrosslanguages.This,ofcourse,wouldresultindifferentmorphosyntactic behaviorsforthelexemesrepresentingthesedifferentconstruals.Forexample,two languagesmayconstruetheexperienceofangerdifferently,withonefocusingon thetemporarystateofbeingangryandtheotherfocusingontheprocessofchange fromanon-angrystatetoanangryone.Astudythatstartsataconceptualbaseline missesthiskindofvariation.Thisthesisaimedtocapturevariationstartingfroma conceptualbaseline,butaswehaveseen,alternativeconstrualsfossilizedinlexical polysemyhavemostlikelyintroducedsomeofthisvariationinconstrual. Therearemanystudiesthathaveaimedtocapturethevariationinconstrual, perhapsmostnotablythepioneeringworkofthePearStories(Chafe1980).Theuse ofvideoallowedparticipantsinthestudytogodirectlyfromexperiencetolinguistic form,withoutapredeterminedsyntacticorconceptualstructure.Forsometargets inthefilm,thelinguisticresultsaresurprisinglyhomogeneous;forothers,agreat dealofvariationresulted.Itwouldbeinterestingtoconductastudyonpartsof speechfromanexperientialbaseline.Thiswouldrequirevideoorpictureelicitation, andagreatdealoftimeandresourcestocollectthedatafrommanydiverse languages.Theresultwouldlikelybemorevariationthanisfoundinthisstudy,but howmuchmore?Wouldthepartsofspeechprototypestructuresemergeinsucha study,orwouldtheybehiddenbytheconfoundingeffectsofvariationinconstrual? 90 6. Conclusions Thisthesishassoughttoillustratetheprototypestructuresofpartsofspeech. Aftercodingmarkednessasymmetriesinelevenlanguages,Iwasabletoapplya multidimensionalscalinganalysistothedatatocreateatwo-dimensionalspatial representationthatcapturesalmostallofthevariation.Themodelshowsclearly threeprototypestructuresrepresentingnouns,adjectives,andverbsthatresult indirectlyfromconceptualdissimilarities.Thenounandverbprototypeclustersare stronger,whiletheadjectiveprototypeclusterisweaker. Inparticular,itistheinteractionofconceptualclassesandsubclasseswith thethreebasicpropositionalactfunctionsthatyieldmarkednessdifferences. Languagesexhibitvariationregardingwhichconceptsbelongtowhichlanguagespecificclassesandregardinghowtheseconceptsareencodedmorphosyntactically. Yetthroughthisvariation,theprototypicalityofobjectsinreference,propertiesin modification,andactionsinpredicationmotivatethestructuresthatareseenclearly inthespatialmodelpresentedhere.Itissignificantenoughtoemphasizeagainthat everylanguageinthisstudywasfoundtoexhibitsomemorphosyntactic distinctionsbetweenobjects,properties,andactions.Thischaracteristicofthedata iscriticalinmotivatingaspatialmodelwiththreedistinctclusters,ratherthan continuousandinseparabledatapointsalongthesemanticcontinuumfromobjects topropertiestoactions. Oneofthemostimportantcontributionsofthisstudyistoourunderstanding ofthesemanticfactorsthatmotivatethepartsofspeechprototypes.Theroleof severalsemanticfactorsidentifiedintheliteratureweresupportedinthisanalysis, 91 suchasrelationality,gradability,transitoriness,andstativity.Theevidencefortheir roleisseenbothqualitativelyinmarkednessdistinctionsofparticularlanguages thatreflecttheirinfluenceandquantitativelyinthearrangementofconceptsinthe spatialmodel.Animacyhasalsobeendiscussedintheliteratureasshapingformal distinctionsamonglexemesrepresentingobjectconcepts,butwasnotincludedasa primarysemanticfactorinpartsofspeechprototypestructures.Theanalysishere hasshownthatitplaysakeyrole,butalsothatthisrolemaybeconfinedtothe internalshapeofthenounprototype.Thisopensthedoorforfutureresearchto explorethedomainofeachsemanticfactor—withinaparticularprototype,shaping theboundarybetweentwoormoreprototypes,orboth. Theseresultssupportandillustratetheprototypetheoryofpartsofspeech. Evenmore,theyareconsistentwithcontemporarytypologicalresearchand specificallymultidimensionalscalinganalysesthatsupportneitheran“extreme universalist”noran“extremerelativist”perspectiveonlanguage(Croft&Poole 2008:31-2).Thevariationinpartsofspeechstrategiescannotbeexplainedinterms ofuniversalcategories,yetthepatternsthatemergeareindicativeofuniversal functionalpressuresthatconstrainthevariationprobabilistically.Thespatialmodel ofpartsofspeechdatapresentedhereillustratestheuniversalconceptualstructure underlyingthemostbasicorganizationofgrammar. 92 LISTOFAPPENDICES AppendixA.Completeconceptsubclassificationandselectedexemplars.....................94 AppendixB.PartsofspeechdatacodedforMDSanalysis....................................................95 AppendixC.MDSoutputtableforpoints...................................................................................112 AppendixD.MDSoutputtableforcuttinglines......................................................................113 93 AppendixA.Completeconceptsubclassificationandselectedexemplars Class(/Type) Humans Kinship Animals Plants/PlantProducts Artifacts BodyParts Places Material/Substance Gender Color Form Age Value Dimension PhysicalProperties HumanPropensity 2-ParticipantStates InactiveActions DurativeProcesses CyclicAchievements (Semelfactives) DirectedAchievements Cognition Desire Emotion Perception Posture Spatial/Possession SocialInteraction Consumption Motion ChangeofState CreationVerbs Emission Contact BodilyMovement Existence ChangeofPossession ChangeofState 94 Exemplars WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK AppendixB.PartsofspeechdatacodedforMDSanalysis KH-RSC-0 WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 KH-RSCVarStr 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 KH-RBPPGN 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 KH-RBP-0 KH-MSC-0 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 95 KH-MSCVarStr 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 KH-MBPSPRL1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 6 6 6 6 6 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 KH-MBPSPRL2 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 1 1 1 1 1 1 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 KH-MBP-0 KH-PSC-0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK KH-PSCCop 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 KH-PBPTAM1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 KH-PBPTAM2 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 KH-PBP-0 KS-RSC-0 KS-RSC-Aff 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 1 9 9 6 6 6 6 6 6 6 6 6 6 1 6 9 1 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 6 9 1 1 1 1 1 1 1 1 1 1 1 6 1 9 6 1 1 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 96 KS-RBPClass 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 1 9 9 6 6 6 6 6 6 6 6 6 6 9 6 9 9 6 6 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 KS-RBPNum 1 1 1 1 1 1 1 1 1 9 1 1 1 1 6 1 9 9 6 6 6 6 6 6 6 6 6 6 9 6 9 9 6 6 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 KS-RBP-0 KS-MSC-0 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 9 1 9 9 1 1 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 6 1 1 1 1 6 1 1 9 1 9 9 6 6 6 6 6 6 6 1 6 1 6 6 6 6 6 6 6 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK KS-MSCSuff 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 6 1 6 1 1 6 9 9 6 9 9 1 1 1 1 1 1 1 9 1 9 1 1 1 1 1 1 1 KS-PSC-0 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 6 6 6 1 1 1 6 1 6 1 1 6 1 1 6 9 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 KS-PSCCop 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 1 1 1 1 1 6 1 1 1 1 6 1 1 1 1 9 1 6 6 1 6 6 6 6 9 6 9 6 6 6 6 6 6 6 KS-PBPTAM 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 6 6 6 1 1 1 6 1 6 1 1 6 1 1 6 9 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 KS-PBP-0 JI-RSC-0 JI-RSC-Aff 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 1 1 1 1 1 6 1 1 1 1 6 1 1 1 1 9 1 6 6 1 6 6 6 6 9 6 9 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 6 6 6 6 6 9 6 6 6 6 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 1 1 9 1 1 1 1 9 1 1 1 1 1 1 97 JI-RBPNGC 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 JI-RBP-0 JI-MSC-0 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 1 9 9 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 9 6 6 6 6 9 6 6 6 6 6 6 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) JI-MSC-Aff JI-MJI-MBPBP-0 NGCagr JI-PSC-0 JI-PSC-Aff JI-PSC-LV 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 9 1 1 1 1 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 9 6 6 6 6 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 9 9 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 9 6 6 1 6 9 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 6 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 9 1 1 6 1 9 1 1 1 1 1 1 WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 9 1 1 1 1 9 1 1 1 1 1 1 98 JI-PBPTAMabl 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 1 6 9 6 6 6 6 6 6 JI-PBPTAMperi 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 9 1 1 6 1 9 1 1 1 1 1 1 JI-PBP-0 MA-RBPNPArt 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 9 6 6 6 6 9 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 1 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK MA-RBPSpecArt 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 MA-RBPNClass 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 MA-RBP-0 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 MAM-SCREL 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 6 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 MA-MSCREL+Pr 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 1 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 99 MA-MSCPossPr 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 1 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 MAM-BPIntens 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 6 9 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 MAM-BP0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 1 9 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 MA-PSC-0 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 MA-PSCCop 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK MA-PBPConcPr 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 6 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 MA-PBP-T1 MA-PBP-T2 MA-PBP-T3 MA-PBP-T4 TO-RSC-0 TO-RSC-Aff 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 6 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 6 6 6 6 6 1 1 1 6 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 6 1 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 100 TO-RBPNum 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 TO-RBPPron 1 1 1 1 6 6 6 6 6 6 6 6 9 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 TO-RBPPLmark 1 1 1 1 1 6 6 6 6 6 6 6 9 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK TO-RBPSubjIndNum 1 1 1 1 1 6 6 6 6 6 6 6 9 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 TO-RBP-0 TO-MSC-0 TO-MSC-Aff TO-MSCRelMar TO-PBPTAM TO-PBP-0 QE-RSC-0 QE-RSC-Aff QE-MSC-0 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 6 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 101 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) QE-MSCSuff QE-MBPSPRL QE-MBP-0 QE-PSC-0 QE-PSCCop QE-PBPTAM 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK 102 QE-PBPTAMperi 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 CR-RSC-0 CR-RSC-Aff CR-RBPNum 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK CR-RBPDim 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 CR-RBP-0 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 CR-MSCAttach 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 CR-MSC-Aff 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 CR-MSCAffLgr 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 103 CR-MBPCMPR 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 CR-MBP-0 CR-PSC-0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 CR-PSCDURAff 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 CR-PSCCop 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) CR-PBPTAM WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 CR-PBPTAMperi 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 KI-RSC-0 KI-RSC-Aff 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 1 1 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 6 6 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 KI-RBPNumSuff 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 104 KI-RBPNumPeri 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 KI-RBPNumVindex 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 9 9 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 KI-RBP-0 KI-MSC-0 KI-MSCGEN 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) KI-MSCREL KI-MBPCMPR KI-MBP-0 KI-PSC-0 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK KI-PSCPlusNoun 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 105 KI-PSCFOC 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 KI-PBPTAMIndArg 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 KI-PBP-0 EN-RSC-0 EN-RSC-Aff 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 1 1 6 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 6 1 1 6 1 1 1 1 1 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) EN-RSCPeri WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 EN-RBPNumAbl 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 EN-RBPNumSuff 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 EN-RBP-0 EN-MSC-0 EN-MSC-Aff EN-MSCREL 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 1 1 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 106 EN-MBPCMPRAbl 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 EN-MBPCMPRSuff 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 6 1 1 1 6 6 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 EN-MBPCMPRSuff2 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK EN-MBPCMPRPeri 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 EN-MBPSPRLAbl 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 EN-MBPSPRLSuff 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 6 6 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 EN-MBPSPRLSuff2 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 EN-MBP-0 EN-PSC-0 EN-PSCCop EN-PEN-PSCBPCopArt TAbl EN-PBPTaff 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 6 6 6 6 6 6 1 6 1 6 1 1 6 6 107 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 1 1 1 1 1 1 6 1 6 1 6 6 1 1 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) EN-PBPTPeri WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 EN-PBPP+NSuff 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 EN-PBPP+MPeri 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 IN-RSC-0 IN-RSC-Aff 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 1 6 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 108 IN-RBPCaseNum 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 IN-RBP-0 IN-MSC-0 IN-MSC-Aff IN-MBPCMPR 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 1 6 6 6 6 1 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK IN-MBPGendAgr 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 IN-MBPNumAgr 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 IN-MBPCaseAgr 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 IN-MBP-0 IN-PSC-0 IN-PSCComp IN-PSCCop IN-PBPIter IN-PBPAux 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 6 1 6 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 1 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 1 6 1 6 6 1 6 109 IN-PBPIndNum 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 6 1 6 6 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK IN-PBPIndGend 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 1 1 6 1 1 1 1 1 1 6 1 1 1 IN-PBPSppl 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 1 6 1 6 6 6 1 1 6 IN-PBPFullAkt 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 6 1 6 1 1 1 1 1 IN-PBPPartAkt 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 1 6 6 6 6 6 IN-PBPRestrAkt 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 110 KA-RSC-0 KA-RSC-Aff KA-RBP-PL KA-RBP-0 KA-MSC-0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 9 1 9 9 9 1 9 9 9 9 9 9 9 9 1 1 6 1 9 9 6 1 6 1 6 1 6 6 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 AppendixB.PartsofspeechdatacodedforMDSanalysis(cont’d) KA-MSC-Aff KA-MBP-PL KA-MBPIntens KA-MBP-0 KA-PSC-O 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 1 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 9 9 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 6 6 6 6 6 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 1 1 9 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 6 6 9 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 1 1 1 1 1 1 1 1 1 1 1 6 1 1 1 9 9 1 1 1 6 1 1 6 1 1 1 6 WOMAN BOY FATHER SISTER DOG BIRD TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK 111 KA-PSCCompVerb 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 6 6 9 9 1 6 6 1 1 6 1 6 1 6 1 KA-PBPTAMDir 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 9 9 1 1 1 6 1 1 6 1 1 1 6 KA-PBPTAMIndir 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 6 6 9 9 1 6 6 1 1 6 1 6 1 6 1 KA-PBP-0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 9 9 6 6 6 6 6 6 6 6 6 6 6 AppendixC.MDSoutputtableforpoints TREE SEED HAT BED ARM FACE RIVER HOUSE WATER STONE MALE FEMALE WHITE RED ROUND FLAT OLD YOUNG GOOD BAD BIG SHORT HEAVY SOFT HAPPY ANGRY KNOW WANT LOVE SEE/LOOKAT SIT WEAR FIGHT EAT COME COOK BUILD COUGH HIT WAVE DIE GIVE BREAK 90 91 94 88 94 96 97 97 97 93 97 97 90 97 97 97 67 67 91 85 75 86 92 95 91 89 92 93 85 91 90 88 104 102 98 103 95 89 101 99 94 95 93 100 98 85 97 100 103 0.006000001 0.022000002 0.021000002 0.012000001 0.030000001 0.032000002 0.047000002 0.048000004 0.047000002 0.049000002 0.049000002 0.050000001 0.050000001 0.050000001 0.025 0.011000001 0.002 0.015000001 0.100000001 0.018000001 0.003 0.027000001 0.150000006 0.026000001 0.149000004 0.213000014 0.032000002 0.181000009 0.045000002 0.014 0.003 0.002 0.005 0.018000001 0.003 0.020000001 0.006000001 0.007 0.042000003 0.011000001 0.002 0.004 0.017000001 0.012000001 0.012000001 0.014 0.003 0.010000001 0.037 112 0.861697508 0.859375265 0.834974071 0.834398446 0.853451011 0.817363695 0.797914134 0.797914134 0.797914134 0.797914134 0.797914134 0.797914134 0.797914134 0.797914134 0.792363729 0.769228641 0.33050118 0.33050118 0.171691973 0.339187447 -0.321879643 0.10800206 0.001233645 0.108121724 0.001233645 -0.101191287 0.109490557 0.017681246 -0.098314302 -0.08870984 -0.284728932 -0.08686224 -0.763423849 -0.723274315 -0.720340106 -0.726102476 -0.859664189 -0.859962073 -0.808105513 -0.789116407 -0.913218145 -0.773803421 -0.823639237 -0.789116407 -0.899269646 -0.777703801 -0.837063773 -0.785703816 -0.829541173 coord2D BIRD 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 1 0 1 1 2 5 0 0 2 0 3 3 2 3 4 1 0 0 1 2 1 1 0 3 0 0 1 3 0 coord1D DOG 2 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 2 0 1 1 0 1 1 2 5 1 0 1 1 0 2 0 0 2 0 1 0 2 1 7 5 0 3 4 2 2 0 1 volume SISTER 75 74 72 73 72 70 69 69 69 64 69 69 64 69 68 69 42 44 71 76 50 62 70 68 69 65 71 70 72 73 62 67 63 64 62 65 59 54 65 64 67 65 64 63 67 55 69 66 65 correctNay FATHER 49 48 46 45 47 44 39.5 39.5 39.5 39.5 39.5 39.5 39.5 39.5 35 34 31.5 31.5 30 33 18 27 24.5 28 24.5 20 29 26 21 22 19 23 14 16 17 15 4 3 8 9 1 13 7 10 2 12 5 11 6 wrongNay BOY wrongYea WOMAN correctYea rank 0.428549482 0.417361899 0.405622173 0.418269535 0.384299431 0.377763505 0.33058484 0.33058484 0.33058484 0.33058484 0.33058484 0.33058484 0.33058484 0.33058484 0.321871172 0.286396204 -0.467034192 -0.467034192 -0.508718317 -0.515571356 -0.817196723 -0.853864506 -0.720621782 -0.854013585 -0.720621782 -0.71706093 -0.868232146 -0.747266058 -0.55680758 -0.542168107 -0.532321416 -0.511127201 0.212456871 0.223196688 0.226282023 0.224426089 0.190334549 0.189018745 0.379253998 0.287659753 0.27430276 0.490559158 0.371566355 0.287659753 0.282302936 0.437822889 0.392426895 0.289630448 0.31672256 AppendixD.MDSoutputtableforcuttinglines wrongNay correctNay PRE normVector1D normVector2D midpoints wrongYea correctYea KH.R.SC.0 KH.R.SC.VarStr KH.R.BP.PGN KH.R.BP.0 KH.M.SC.0 KH.M.SC.VarStr KH.M.BP.SPRL1 KH.M.BP.SPRL2 KH.M.BP.0 KH.P.SC.0 KH.P.SC.Cop KH.P.BP.TAM1 KH.P.BP.TAM2 KH.P.BP.0 KS.R.SC.0 KS.R.SC.Aff KS.R.BP.Class KS.R.BP.Num KS.R.BP.0 KS.M.SC.0 KS.M.SC.Suff KS.P.SC.0 KS.P.SC.Cop KS.P.BP.TAMext KS.P.BP.0 JI.R.SC.0 JI.R.SC.Aff JI.R.BP.NGC JI.R.BP.0 JI.M.SC.0 JI.M.SC.Aff JI.M.BP.NGCagr JI.M.BP.0 JI.P.SC.0 JI.P.SC.Aff JI.P.SC.LV JI.P.BP.TAM.abl JI.P.BP.TAM.peri JI.P.BP.0 MA.R.BP.NPArt MA.R.BP.SpecArt MA.R.BP.NClass MA.R.BP.0 MA.M.SC.REL MA.M.SC.REL.Prep MA.M.SC.PossPron MA.M.BP.Intens 17 31 17 31 31 17 1 31 16 31 17 17 14 16 15 28 15 14 27 12 21 23 28 23 28 16 15 18 29 14 31 30 15 15 16 14 1 14 30 17 29 29 19 31 17 17 12 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 3 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 3 0 0 0 0 1 0 2 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 30 15 30 15 15 30 46 15 31 15 30 30 32 31 27 15 27 27 15 17 3 22 15 22 15 15 16 29 18 15 14 15 30 29 29 30 44 30 15 31 19 19 29 17 31 31 35 1 0.933333333 1 0.933333333 0.933333333 1 1 0.9375 1 0.933333333 1 1 0.933333333 1 0.833333333 0.833333333 1 0.928571429 1 0.923076923 0.75 0.909090909 0.875 0.909090909 0.875 1 1 1 1 1 1 1 1 0.933333333 1 0.928571429 1 0.928571429 1 1 1 1 1 1 1 1 0.916666667 0.805007101 0.135342226 0.805007101 0.135342226 0.135342226 0.805007101 0.083924075 0.886533834 0.759709206 0.135342226 0.805007101 0.815400825 0.61189348 0.759709206 0.905904266 0.901715792 0.759709206 0.131411632 0.759709206 0.833042122 0.069839772 0.858824074 0.798757173 0.858824074 0.798757173 0.815400825 0.759709206 0.910031919 0.848618008 0.815400825 0.077290281 0.815400825 0.815400825 0.392207382 0.759709206 0.140628632 0.903766448 0.140628632 0.815400825 0.966883805 0.871261763 0.871261763 0.871261763 0.966883805 0.966883805 0.966883805 0.105814136 0.593265174 0.990798911 0.593265174 0.990798911 0.990798911 0.593265174 -0.996472152 -0.462663767 0.650262964 0.990798911 0.593265174 -0.578896791 -0.790940181 0.650262964 -0.42348254 -0.432329307 0.650262964 0.991327889 0.650262964 0.553209566 0.997558222 0.512270642 -0.601653537 0.512270642 -0.601653537 -0.578896791 0.650262964 0.414538184 0.529006122 -0.578896791 -0.997008632 -0.578896791 -0.578896791 0.919876823 0.650262964 -0.990062416 -0.428025943 -0.990062416 -0.578896791 -0.255216983 -0.490818643 -0.490818643 -0.490818643 -0.255216983 -0.255216983 -0.255216983 -0.994385925 -0.103897367 0.384659862 -0.103897367 0.384659862 0.384659862 -0.103897367 0.865474831 0.548464427 -0.052589374 0.384659862 -0.103897367 -0.322512825 0.245815811 -0.052589374 0.554941479 0.553038699 -0.052589374 0.384872399 -0.052589374 -0.403280106 -0.839075576 -0.362859967 0.237371388 -0.362859967 0.237371388 0.144504531 -0.052589374 -0.151896832 -0.236404902 -0.322512825 0.132589649 -0.322512825 -0.322512825 -0.10554094 0.359377534 -0.307058682 -0.938371886 -0.307058682 -0.322512825 0.452026892 0.181535394 0.181535394 0.181535394 0.452026892 0.452026892 0.452026892 0.499294263 113 AppendixD.MDSoutputtableforcuttinglines(cont’d) wrongNay correctNay PRE normVector1D normVector2D midpoints wrongYea correctYea MA.M.BP.0 MA.P.SC.0 MA.P.SC.Cop MA.P.BP.ConcPron MA.P.BP.T1 MA.P.BP.T2 MA.P.BP.T3 MA.P.BP.T4 TO.R.SC.0 TO.R.SC.Aff TO.R.BP.Num TO.R.BP.Pron TO.R.BP.PLmark TO.R.BP.SubjIndN TO.R.BP.0 TO.M.SC.0 TO.M.SC.Aff TO.M.SC.RelMark TO.P.BP.TAM TO.P.BP.0 QE.R.SC.0 QE.R.SC.Aff QE.M.SC.0 QE.M.SC.Suff QE.M.BP.SPRL QE.M.BP.0 QE.P.SC.0 QE.P.SC.Cop QE.P.BP.TAM QE.P.BP.TAMperi CR.R.SC.0 CR.R.SC.Aff CR.R.BP.Num CR.R.BP.Dim CR.R.BP.0 CR.M.SC.Attach CR.M.SC.Aff CR.M.SC.AffLgr CR.M.BP.CMPR CR.M.BP.0 CR.P.SC.0 CR.P.SC.DURAff CR.P.SC.Cop CR.P.BP.TAM CR.P.BP.TAMperi KI.R.SC.0 KI.R.SC.Aff 35 19 29 31 3 11 1 31 15 30 15 4 5 5 30 31 17 30 30 17 16 21 31 37 16 36 21 31 21 31 16 31 2 16 31 14 36 11 20 27 17 14 30 31 30 18 26 0 0 0 0 1 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 29 19 17 43 33 45 17 30 15 30 41 40 40 15 16 30 17 17 30 18 16 18 12 33 13 28 18 28 18 31 16 45 31 16 33 11 36 27 20 30 33 17 16 17 26 18 0.916666667 1 1 1 0.5 0.636363636 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.105869247 0.871261763 0.871261763 0.966883805 0.996973736 0.148875981 0.906657705 0.815400825 0.759709206 0.759709206 0.759709206 0.551009719 0.72049683 0.72049683 0.759709206 0.815400825 0.832039125 0.832039125 0.832039125 0.832039125 0.906241601 0.759709206 0.845659021 0.439234795 0.657924726 0.710980511 0.746366578 0.845659021 0.746366578 0.845659021 0.759709206 0.759709206 0.758010496 0.759709206 0.759709206 0.077290281 0.778488432 0.778488432 0.218296833 0.218296833 0.815400825 0.077290281 0.815400825 0.759709206 0.815400825 0.910031919 0.910031919 -0.994380059 -0.490818643 -0.490818643 -0.255216983 -0.077739114 -0.988855875 -0.421867048 -0.578896791 0.650262964 0.650262964 0.650262964 0.834498825 0.69345823 0.69345823 0.650262964 -0.578896791 0.55471695 0.55471695 0.55471695 0.55471695 -0.422760169 0.650262964 -0.533723543 -0.898372303 -0.753083697 -0.703211712 -0.665535071 -0.533723543 -0.665535071 -0.533723543 0.650262964 0.650262964 0.652242354 0.650262964 0.650262964 -0.997008632 -0.627658953 -0.627658953 0.975882418 0.975882418 -0.578896791 -0.997008632 -0.578896791 0.650262964 -0.578896791 0.414538184 0.414538184 0.499292599 0.181535394 0.181535394 0.452026892 -0.867734984 -0.319300761 -0.929610446 -0.322512825 -0.052589374 -0.052589374 -0.052589374 0.79789426 0.868118196 0.868118196 -0.052589374 -0.322512825 0.003333235 0.003333235 0.003333235 0.003333235 0.011581476 -0.052589374 0.104845334 0.499757669 0.286430384 0.342388845 0.302672077 0.104845334 0.302672077 0.104845334 -0.052589374 -0.052589374 0.91607827 -0.052589374 -0.052589374 0.132589649 -0.790927946 -0.790927946 0.064665113 0.064665113 -0.322512825 0.132589649 -0.322512825 -0.052589374 -0.322512825 -0.151896832 -0.151896832 114 AppendixD.MDSoutputtableforcuttinglines(cont’d) wrongNay correctNay PRE normVector1D normVector2D midpoints wrongYea correctYea KI.R.BP.Num.Suff KI.R.BP.Num.Peri KI.R.BP.Num.Vind KI.R.BP.0 KI.M.SC.0 KI.M.SC.GEN KI.M.SC.REL KI.M.BP.CMPR KI.M.BP.0 KI.P.SC.0 KI.P.SC.PlusNoun KI.P.SC.FOC KI.P.BP.TAMIndArg KI.P.BP.0 EN.R.SC.0 EN.R.SC.Aff EN.R.SC.Peri EN.R.BP.NumAbl EN.R.BP.NumSuff EN.R.BP.0 EN.M.SC.0 EN.M.SC.Aff EN.M.SC.REL EN.M.BP.CMPRAbl EN.M.BP.CMPRSuff EN.M.BP.CPRSuff2 EN.M.BP.CMPRPeri EN.M.BP.SPRLAbl EN.M.BP.SPRLSuff EN.M.BP.SPRLSuff2 EN.M.BP.0 EN.P.SC.0 EN.P.SC.Cop EN.P.SC.CopArt EN.P.BP.TAbl EN.P.BP.Taff EN.P.BP.TPeri EN.P.BP.P+N.Suff EN.P.BP.P+N.Peri IN.R.SC.0 IN.R.SC.Aff IN.R.BP.CaseNum IN.R.BP.0 IN.M.SC.0 IN.M.SC.Aff IN.M.BP.CMPR IN.M.BP.GendAgr 2 14 6 28 15 16 17 14 35 32 0 15 17 31 19 30 0 1 15 33 28 36 17 0 10 0 1 0 11 0 35 17 18 14 11 3 32 17 32 16 33 32 17 16 34 14 1 0 2 0 0 0 0 0 0 0 1 0 0 0 0 2 3 0 0 1 0 0 2 0 0 3 0 0 0 2 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 1 0 0 2 0 1 0 2 0 1 0 0 0 0 0 3 0 0 0 2 0 0 0 0 1 0 1 42 28 38 16 33 32 31 34 14 15 47 33 31 17 28 16 47 48 33 16 20 11 32 47 36 48 48 47 36 48 14 32 31 35 34 43 17 32 17 31 16 17 32 33 14 35 47 1 0.857142857 1 1 1 1 1 1 1 0.9375 0 1 1 1 0.894736842 0.842105263 0 1 0.933333333 1 0.95 0.846153846 1 0 0.7 0 1 0 0.818181818 0 1 1 1 1 0.636363636 0.5 1 1 1 0.888888889 1 1 1 1 0.928571429 1 0.5 0.758010496 0.134894412 0.726753379 0.759709206 0.108156838 0.759709206 0.845659021 0.03816815 0.03816815 0.905462452 0.995343772 0.750974118 0.845659021 0.845659021 0.836046157 0.894682439 0.997660913 0.080235799 0.13658569 0.759709206 0.705045934 0.004674728 0.815400825 0.994291391 0.101960971 0.148835634 0.122816239 0.994291391 0.105237598 0.148835634 0.03816815 0.815400825 0.157747437 0.746614471 0.35504321 0.759653708 0.815400825 0.815400825 0.815400825 0.888541909 0.759709206 0.815400825 0.815400825 0.077290281 0.679071109 0.03816815 0.112080302 0.652242354 0.990859979 0.686898483 0.650262964 -0.994133843 0.650262964 -0.533723543 -0.999271331 -0.999271331 -0.424426376 0.096388671 0.66033164 -0.533723543 -0.533723543 0.548659115 -0.446702734 0.068357168 0.996775911 0.99062826 0.650262964 -0.70916164 -0.999989073 -0.578896791 0.106698784 -0.9947884 0.988861949 0.992429429 0.106698784 -0.994447107 0.988861949 -0.999271331 -0.578896791 -0.987479491 0.665256966 -0.934849891 -0.650327797 -0.578896791 -0.578896791 -0.578896791 -0.458795461 0.650262964 -0.578896791 -0.578896791 -0.997008632 -0.734072496 -0.999271331 0.993699153 0.91607827 0.384684387 0.851460168 -0.052589374 0.498570448 0.359377534 0.104845334 0.493397837 0.493397837 0.57581991 0.903267054 0.778518423 0.104845334 0.104845334 -0.360463655 0.549675992 0.893144248 0.493979893 0.38459134 0.359377534 0.301908103 0.530793426 -0.322512825 0.906817239 0.500063737 -0.858103443 -0.849346925 0.906817239 0.499311583 -0.858103443 0.493397837 -0.322512825 -0.19994926 0.812687789 -0.467402502 -0.872386957 -0.322512825 -0.322512825 -0.322512825 0.546715708 0.359377534 -0.322512825 -0.322512825 0.132589649 0.314391842 0.493397837 -0.84931586 115 AppendixD.MDSoutputtableforcuttinglines(cont’d) correctYea wrongYea wrongNay correctNay PRE normVector1D normVector2D midpoints IN.M.BP.NumAgr IN.M.BP.CaseAgr IN.M.BP.0 IN.P.SC.0 IN.P.SC.Comp IN.P.SC.Cop IN.P.BP.Iter IN.P.BP.Aux IN.P.BP.IndNum IN.P.BP.IndGend IN.P.BP.Suppletive IN.P.BP.FullAkt IN.P.BP.PartAkt IN.P.BP.RestrAkt KA.R.SC.0 KA.R.SC.Aff KA.R.BP.PL KA.R.BP.0 KA.M.SC.0 KA.M.SC.Aff KA.M.BP.PL KA.M.BP.Intens KA.M.BP.0 KA.P.SC.O KA.P.SC.CompVerb KA.P.BP.TAMDir KA.P.BP.TAMIndir KA.P.BP.0 1 33 16 15 1 32 1 3 2 12 3 15 1 32 24 30 31 15 30 31 1 13 31 41 6 11 6 32 0 0 0 2 0 0 1 1 0 2 1 2 0 0 1 0 0 0 0 0 0 0 0 1 1 3 1 0 0 0 0 0 1 0 1 1 0 0 2 0 1 0 2 0 0 0 1 1 0 0 0 1 2 0 2 0 48 16 33 32 47 17 46 44 47 35 43 32 47 17 6 16 15 31 16 15 46 31 13 4 38 33 38 15 1 1 1 0.866666667 0.5 1 0 0.5 1 0.833333333 0.4 0.866666667 0.5 1 0.571428571 1 1 1 0.9375 0.933333333 1 1 1 0.6 0.625 0.727272727 0.625 1 0.083924075 0.759709206 0.759709206 0.146680095 0.840059075 0.815400825 0.953009227 0.941653131 0.840928597 0.26358795 0.950644026 0.146680095 0.840059075 0.815400825 0.145426729 0.759709206 0.815400825 0.815400825 0.807219254 0.609497902 0.083924075 0.077290281 0.077290281 0.919650781 0.940998987 0.137246678 0.940998987 0.815400825 -0.996472152 0.650262964 0.650262964 -0.989183982 -0.542494931 -0.578896791 0.302941269 -0.33658488 -0.541146093 -0.964635368 -0.310283639 -0.989183982 -0.542494931 -0.578896791 -0.989369024 0.650262964 -0.578896791 -0.578896791 -0.590251706 -0.792787682 -0.996472152 -0.997008632 -0.997008632 0.392737115 -0.338409378 -0.9905369 -0.338409378 -0.578896791 0.865474831 0.359377534 0.359377534 -0.306770662 -0.916119457 -0.322512825 -0.76640673 -0.896301325 -0.916224298 -0.408055624 -0.892136433 -0.306770662 -0.916119457 -0.322512825 -0.403838328 0.359377534 -0.322512825 -0.322512825 0.228067302 0.2454613 0.865474831 0.132589649 0.132589649 -0.61577581 -0.882509908 -0.318698564 -0.882509908 -0.322512825 116 REFERENCES Anderson,LloydB.1974.Distinctsourcesoffuzzydata:waysofintegrating relativelydiscreteandgradientaspectsoflanguage,andexplaininggrammaron thebasisofsemanticfields.InRogerW.Shuy&Charles-JamesN.Bailey(eds.), 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