* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Meaning representation, semantic analysis, and lexical semantics
Macedonian grammar wikipedia , lookup
Portuguese grammar wikipedia , lookup
Compound (linguistics) wikipedia , lookup
Preposition and postposition wikipedia , lookup
Kannada grammar wikipedia , lookup
Internalism and externalism wikipedia , lookup
Transformational grammar wikipedia , lookup
Spanish grammar wikipedia , lookup
Old English grammar wikipedia , lookup
Scottish Gaelic grammar wikipedia , lookup
Morphology (linguistics) wikipedia , lookup
Integrational theory of language wikipedia , lookup
Untranslatability wikipedia , lookup
Dependency grammar wikipedia , lookup
Ancient Greek grammar wikipedia , lookup
Malay grammar wikipedia , lookup
Polish grammar wikipedia , lookup
Serbo-Croatian grammar wikipedia , lookup
Icelandic grammar wikipedia , lookup
Focus (linguistics) wikipedia , lookup
Word-sense disambiguation wikipedia , lookup
Symbol grounding problem wikipedia , lookup
Latin syntax wikipedia , lookup
Meaning (philosophy of language) wikipedia , lookup
Construction grammar wikipedia , lookup
Semantic memory wikipedia , lookup
Junction Grammar wikipedia , lookup
Semantic holism wikipedia , lookup
Pipil grammar wikipedia , lookup
Semantics Ling 571 Fei Xia Week 6: 11/1-11/3/05 Outline • Meaning representation: what formal structures should be used to represent the meaning of a sentence? • Semantic analysis: how to form the formal structures from smaller pieces? • Lexical semantics: Meaning representation Meaning representation • Requirements that meaning representations should fulfill • Types of meaning representation: – First order predicate calculus (FOPC) – Frame-based representation – Semantic network – Conceptual dependency diagram Requirements • • • • • Verifiability Unambiguous representations Canonical form Inference Expressiveness Verifiability • A system's ability to compare the state of affairs described by a representation to the state of affairs in some world as modeled in a knowledge base • Example: – Sent: Maharani serves vegetarian dishes. – Question: Is the statement true? Unambiguous representation • Representations should have a single unambiguous interpretation. • Example: – Mary and John bought a book – Two students met three teachers – A German teacher – A Chinese restaurant – A Canadian restaurant Canonical form • Sentences with the same thing should have the same meaning representation • Example: – Alternations: active/passive, dative shift – Does Maharani have vegetarian dishes? – Do they serve vegetarian food at Maharani? Inference • a system's ability to draw valid conclusions based on the meaning representation of inputs and its store of background knowledge. • Example: – Sent: Maharani serves vegetarian dishes – Question: can vegetarians eat at Maharani? Expressiveness • A system should be expressive enough to handle an extremely wide range of subject matter. • Example: – Belief: I think that he is smart. – Hypothetical statement: If I were you, I would buy that book. – Former president, fake ID, allegedly, apprarently Meaning representation • Requirements – – – – – Verifiability Unambiguous representations Canonical form Inference Expressiveness • Types of meaning representation: – – – – First order predicate calculus (FOPC) Frame-based representation Semantic network Conceptual dependency diagram FOPC • Elements of FOPC • Representing – Categories – Events – Time (including tense) – Aspect – Belief –… Elements of FOPC • Terms: – Constant: specific objects in the world: e.g., Maharani – Variable: a particular unknown object or an arbitrary object: e.g., a restaurant – Function: concepts: e.g., LocationOf(Maharani) • Predicates: referring to relations that hold among objects: – Ex: Serve(Maharani, food) – Arguments of predicates must be terms. Elements of FOPC (cont) • Logical connectives: • Quantifier: ,, , • Example: All restaurants serve food. x Re staurant ( x) Serve( x, food ) Inference rules • Modus ponens: • Conjunction: • Disjunction: • Simplification: • …. FOPC • Elements of FOPC • Representing – Categories – Events – Time – Aspect – Belief –… Representing time • • • • • • Past perfect: I had arrived in NY Simple past: I arrived in NY Present perfect: I have arrived in NY Present: I arrive in NY Simple future: I will arrive in NY Future perfect: I will have arrived in NY Representing time (cont) • Reichenbach’s approach – E: the time of the event – U: the time of the utterance – R: the reference point • Example: – Past perfect: I had arrived: E > R > U – Simple past: I arrived: E=R > U – Present perfect: I have arrived: E > R=U Aspect • Four types of event expression: – – – – Stative: I like books. I have a ticket Activity: She drove a Mazda. I live in NY Accomplishment: Sally booked her flight. Achievement: He reached NY. • Differences: – Being in a state or not – occurring at a given time, or over some span of a time – Resulting in a state: happening in an instant or not. Distinguishing four types • Allowing progressive, imperative – *I am liking books. – *Like books. • Modified by in-phrase, for-phrase: in a month, for a mont – He lived in NY for five years. – *He reached NY for five minutes. Distinguishing four types (cont) • “Stop” test: stop doing something – *He stopped reaching NY. – He stopped booking the ticket • Modified by adverbs such as “deliberately”, “carefully” – *He likes books deliberately Representing beliefs • John believes that Mary ate lunch. • One possibility: u, v, IsA(u, believing ) IsA(v, Eating ) Believer (u, John) Believed Pr op(u, v) Eater(v, Mary ) Eaten(v, lunch ) • Another possibility: Believing ( John, Eating ( Mary , lunch )) Representing beliefs (cont) • Substitution does not work • Example: – John knows Flight 1045 is delayed – Mary is on Flight 1045 – Does John know that Mary’s flight was delayed? FOPC is not sufficient. Use modal logic Summary of meaning representation • Five requirements: – – – – – Verifiability Unambiguous representations Canonical form Inference Expressiveness • Four types of representations: – – – – First order predicate calculus (FOPC) Frame-based representation Semantic network Conceptual dependency diagram Outline • Meaning representation: • Semantic analysis: how to form the formal structures from smaller pieces? • Lexical semantics: Semantic analysis Semantic analysis • Goal: to form the formal structures from smaller pieces • Three approaches: – Syntax-driven semantic analysis – Semantic grammars – Information extraction: filling templates Syntax-driven approach • Parsing then semantic analysis, or parsing with semantic analysis. • Semantic augmentations to grammars (e.g., CFG or LTAG) – Associate FOPC expression with lexical items – Use exp ression (xP( x))( A) P( A) – Use complex-terms exp ression • Sentence: AyCaramba serves meat • Goal: eIsA(e, serving ) Server(e, AyCaramba) Served (e, Meat ) • Augmented rules: V serves {xye IsA(e, serving ) Server(e, y ) Served (e, x)} VP V NP {V .sem( NP.sem)} S NP VP {VP.sem( NP.sem)} NP N {N .sem} N AyCaramba { AyCaramba} N meat {meat} Quantifiers • Sentence: A restaurant serves meat • Goal: ex IsA( x, Re staurant ) IsA(e, Serving ) Server(e, x) Served (e, Meat ) • Augmented rules: Det a N ' N N restaurant NP Det N ' a restaurant {} x IsA( x, N .sem)} {restaurant} {Det .sem x N '.sem( x)} x IsA( x, restaurant ) Complex terms • Current formula: e IsA(e, Serving ) Server(e, xIsA( x, Re staurant )) Served (e, Meat ) • Goal: ex IsA( x, Re staurant ) IsA(e, Serving ) Server(e, x) Served (e, Meat ) • What is needed: ( P(..., x body ,...)) (x body P(..., x,...)) ( P(..., x body ,...)) (x body P(..., x,...)) Quantifier scoping • Sentence: Every restaurant has a menu • Formula with complex terms e IsA(e, Having ) Haver(e, x IsA( x, Re staurant ) ) Had (e, y IsA( y, menu) ) • Reading 1: x IsA( x, Re staurant ) e y IsA( y, menu) IsA(e, Having ) Haver(e, x) Had (e, y ) • Reading 2: y IsA( y, menu) x IsA( x, Re staurant ) (e IsA(e, Having ) Haver (e, x) Had (e, y )) Semantic analysis • Goal: to form the formal structures from smaller pieces • Three approaches: – Syntax-driven semantic analysis – Semantic grammar – Information extraction: filling templates Semantic grammar • Syntactic parse trees only contain parts that are unimportant in semantic processing. • Ex: Mary wants to go to eat some Italian food • Rules in a semantic grammar – InfoRequest USER want to go to eat FOODTYPE – FOODTYPENATIONALITY FOODTYPE – NATIONALITYItalian/Mexican/…. Semantic grammar (cont) Pros: • No need for syntactic parsing • Focus on relevant info • Semantic grammar helps to disambiguate Cons: • The grammar is domain-specific. Information extraction • The desired knowledge can be described by a relatively simple and fixed template. • Only a small part of the info in the text is relevant for filling the template. • No full parsing is needed: chunking, NE tagging, pattern matching, … • IE is a big field: e.g., MUC. KnowItAll Summary of semantic analysis • Goal: to form the formal structures from smaller pieces • Three approaches: – Syntax-driven semantic analysis – Semantic grammar – Information extraction Outline • Meaning representation • Semantic analysis • Lexical semantics Lexical semantics What is lexical semantics? • Meaning of word: word senses • Relations among words: • Predicate-argument structures • Thematic roles • Selectional restrictions • Mapping from conceptual structures to grammatical functions • Word classes and alternations Important resources • • • • • • • Dictionaries Ontology and taxonomy WordNet FrameNet PropBank Levin’s English verb classes …. Meaning of words • Lexeme is an entry in the lexicon that includes – Orthographic form – Phonological form – Sense: lexeme’s meaning Relations among lexemes • Homonyms: same orth. and phon. forms, but different, unrelated meanings – bank vs. bank • Homophones: same phon. different orth – read vs. red, to, two, and too. • Homographs: same orth, different phon. – bass vs. bass Polysemy • Word with multiple but related meanings – He served his time in prison – He served as U.N. ambassador – They rarely served lunch after 3pm. • What’s the difference between polysemy and homonymy: – Homonymy: distinct, unrelated meanings – Polysemy: distinct but related meanings – How to decide: etymology, notion of coincidence Synonymy • Different lexemes with the same meaning • Substitutable in some environment: – How big is that plane? – How large is that plane? • What influences substitutablity? – – – – Polysemy: big brother vs. large brother Subtle shade of meaning: first class fare/?price Colllocational constraints: big/?large mistake Register: social factors Hyponymy • General: hypernym – “vehicle” is a hypernym of “car” • Specific: hyponym – “car” is a hyponym of “vehicle”. • Test: X is a car implies that X is a vehicle. Ontology and taxonomy • Ontology: – It is a specification of a conceptualization of a knowledge domain – It is a controlled vocabulary that describes objects and the relations between them in a formal way, and has strict rules about how to specify terms and relationships. • Taxonomy: – A taxonomy is a hierarchical data structure or a type of classification schema made up of classes, where a child of a taxonomy node represents a more restricted, smaller, subclass than its parent. – a particular arrangement of the elements of an ontology into a tree-like class inclusion structure. WordNet • Most widely used lexical database for English • Developed by George Miller etc. at Princeton • Three databases: Noun, Verb, Adj/Adv • Each entry in a database: a unique orthographic form + a set of senses • Synset: a set of synonyms • http://www.cogsci.princeton.edu/~wn WordNet (cont) • Nouns: – – – – Hypernym: meal, lunch Has-Member: crew, pilot Has-part: table, leg Antonym: leader, follower • Verbs: – Hypernym: travel, fly – Entail: snoresleep – Antonym: increase decrease • Adj/Adv: – Antonym: heavy light, quickly slowly Lexical semantics • Meaning of word: word senses • Relations among words: • Predicate-argument structures • Thematic roles • Selectional restrictions • Mapping from conceptual structures to grammatical functions • Word classes and alternations Predicate-argument structure • Predicate-argument: – Verb/adj as predicate – Nouns etc. as arguments – Example: buy(Mary, book) • Subcategorization frame: – specify number, position, and syntactic category of arguments (or complements) – Example: • (NP, NP): I want Italian food • (NP, Inf-VP): I want to save money • (NP, NP, Inf-VP): I want the book to be delivered tomorrow. Thematic (Semantic) roles • A set of roles: – – – – – Agent: the volitional causer of an event Force: the non-volitional causer of an event Patient/Theme: the one most directly affected by an event Experiencer: the experiencer of an event Others: Instrument, Source, Goal, Beneficiary, … • Example: – John broke a glass – John broke an ankle in the game Selectional restriction • Mary ate the cake • ?The table ate the cake • Mary ate Italian food with her friends. • Mary ate somewhere with her friends. • White house announced that … • The spider assassinated the fly. FrameNet • Developed by Fillmore and Baker at UC Berkeley since 1997. • http://www.icsi.berkeley.edu/~framenet • FrameNet database has two parts: – Frame database: a list of semantic frames, and relations between them, such as frame inheritance and frame composition. – Lexical database: each entry (called a lexical unit) is a (lemma, semantic frame) pair. Semantic frames • Definition • Frame elements (FEs): conceptual structure – Core FEs: Communicator, Medium, Message, Topic – Non-Core FEs: time, place, manner • • • • Inherit from: Subframes: Lexical units: Example sentences: One frame • Frame: Communication – Definition: A Communicator conveys a Message to an Addressee. the Topic and Medium of the communication also may be expressed. – Core FEs: Addressee, Communicator, Medium, Message, Topic – Lexical units: communicate, indicate, signal Another frame Frame: Statement – Inherit from: Communication – Definition: This frame contains verbs and nouns that communicate the act of a Speaker to address a Message to some Addressee using language. – Core FEs: Communicator, Medium, Message, Topic – Lexical units: admit, affirm, express,…. Project status • More than 625 semantic frames, 8900 entries in the lexicon. • Version 1.2 released in June 2005. • Book: “FrameNet: Theory and Practice” (printed June 2005) Proposition Bank (PropBank) • Developed by Palmer and Marcus at UPenn. • http://www.cis.upenn.edu/~ace • Annotate the English Penn Treebank with predicate-argument information • Corpus can be used for automatic labeling of thematic roles Semantic tags • Main tags: – – – – Arg0: Agent Arg1: theme or direct object Arg2: instrument, indirect object … • Secondary tags: – – – – – ArgM-DIR: direction ArgM-LOC: locative ArgM-NEG: negation ArgM-DIS: discourse … Semantic tags (cont) • Main tags are defined based on each verb. • Example: – Buy: John bought a book from Mary for 5 dollars – Sell: Mary sold a book to John for 5 dollars – Pay: John paid Mary 5 dollars for a book. Buy Arg0 buyer Arg1 thing bought Arg2 seller Arg3 price paid Sell seller thing bought buyer price paid Pay buyer price paid seller thing bought Lexical semantics • Meaning of word: word senses • Relations among words: • Predicate-argument structures • Thematic roles • Selectional restrictions • Mapping from conceptual structure to grammatical function • Word classes and alternations Mapping between conceptual structure and grammatical function • Buy: buyer, thing bought, seller, price,…. • Possible syntactic realizations: – (buyer, thing bought): John bought a book – (price, thing bought): $5 can buy two books – (thing bought, seller): The book was bought from Mary – (buyer, thing bought, seller): John bought a book from Mary. – **(buyer, price): John bought $5. Alternations • An alternation is a set of different mappings of conceptual roles to grammatical function. • Example: dative alternation – John gave Mary a book – John gave a book to Mary • Verb classes: give, donate, Levin’s verb classes • Levin (1993): – Verb classes – Alternations – Show the list of alternatives a verb class can take. • Problems: – Many verbs appear in multiple classes – Verbs in the same classes do not behave exactly the same: e.g, (meet, visit), (give, donate),…. Summary of lexical semantics (1) • Meaning of word: word senses • Relations among words: – – – – – – Homonyms: bank, bank Homophones: read. red Homographs: bass, bass Polysemy: bank: blood bank, financial bank Synonyms: big, large Hypernym/Hyponym: vehicle, car • Ontology and taxonomy • WordNet Summary of lexical semantics (2) • Predicate-argument structures • Thematic roles • Selectional restrictions • FrameNet • PropBank Summary of lexical semantics (3) • Mapping from conceptual structures to grammatical functions • Word classes and alternations • Levin’s verb classes for English Summary of semantics • Meaning representation: – Criteria for good representation – First-order predicate calculus (FOPC) • Semantic analysis: – Syntax-based semantic analysis – Semantic grammar – Information extraction • Lexical semantics: – – – – WordNet FrameNet PropBank Levin’s verb classes