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Prepositional Phrase Attachment Problem 03M05601 Ashish Almeida 8 November 2003 PP attachment problem 1 Overview – Introduction to NLP – Analysis in UNL system – Prepositional phrase attachment problem – Proposed method to handle this problem 8 November 2003 PP attachment problem 2 Motivation • Analysis involves many complex problems • Prepositional phrase attachment problem is one such difficult problem. • If solved, improve the quality of information extracted manifold • No existing system solves the problem 8 November 2003 PP attachment problem 3 Tasks involved in NLP Analysis and generation NL understanding Text Meaning NL generation 8 November 2003 PP attachment problem 4 Phases in NLP • Morphological analysis • Syntactic analysis • Semantic analysis • Discourse integration • Pragmatic analysis 8 November 2003 PP attachment problem 5 Is NL Compositional ? • Compsitional expression – Meaning of the whole from meaning of parts e.g. 8 November 2003 strong tea - rich tea day by day - all the time PP attachment problem 6 Analysis Morphological + Syntactic + Semantic analysis • All these phases are dependent on each other. • Interactive Vs modular approach • Analysis in UNL system - interactive 8 November 2003 PP attachment problem 7 UNL … • UNL is Interlingua e.g. Ram ate rice with spoon. @ entry @ present eat(icl>do) agt obj John(iof>person) ins spoon(icl>artifact) rice(icl>food) 8 November 2003 PP attachment problem 8 UNL expresion UNL Expression for Ram ate rice with spoon. agt(eat(icl>do).@past.@entry, Ram(iof>person)) obj(eat(icl>do).@past.@entry, rice(icl>food)) ins(eat(icl>do).@past.@entry, spoon(icl>tool)) agt(eat(icl>do).@past.@entry, Ram(iof>person)) Relation 8 November 2003 UWs PP attachment problem Attributes 9 Analysis in UNL • Enconverter – – – – Natural Language to UNL Handles one sentence at a time Predicate preserving parser Kind of Turing machine • Components – Dictionary : lexical units, uw, semantic attributes – Rule base : head movement rules, relation resolving rules • Working – Uses dictionary and rule bases to process the sentence. 8 November 2003 PP attachment problem 10 Prepositional Phrase Attachment Problem • Type of Structural ambiguity in a sentence Verb attachment JohnNP readVP the reportNP on new technologies.PP Noun attachment 8 November 2003 PP attachment problem 11 Prepositional Phrase Attachment Problem… • Noun attachment Vs verb attachment e.g. John read the report on new technologies. read John * the report John on read the report on new technologies new technologies 8 November 2003 PP attachment problem 12 Establishing semantic relation Same structure-different semantic relation e.g. 1. Ram ate rice with spoon. ……instrument The UNL for this sentence is ins(eat(icl>do).@past.@entry, spoon(icl>tool)) 2. Ram ate rice with Sita. ……co-agent The UNL for this sentence is cag(eat(icl>do).@past.@entry, Sita(iof>person)) 8 November 2003 PP attachment problem 13 Difficult problem • PP attachment problem is simpler or no problem for human being - who use world knowledge to process it. • This world knowledge is not available to machines. e.g. travel by night …time travel by bus …instrument 8 November 2003 PP attachment problem 14 Different sites of attachment – The search for the policy is going on. – The test will be held at the end of August. – In August 1947, India became free from British rule. – Wilson received a medal from the commanding officer at a farewell party. • There is no restriction on how far the PP can lie from the word to which it relates. 8 November 2003 PP attachment problem 15 Affinity with preceding phrase • The preposition of gets attached to a noun phrase or a verb phrase immediately preceding it. – – – – They were involved in the murder of a 90-year-old woman. It was begun last week by the crew of a giant crane-barge. He died of an overdose of sleeping pills The system will be tailored to meet the need of the political party. 8 November 2003 PP attachment problem 16 Existing methods • generate mod-obj combination for almost all PP relations – E.g He came according to his promise. agt(come(icl>do)@past.@entry, he) *mod(come(icl>do)@past.@entry, :01) obj:01(according to, promise(icl>abstract thing)) mod:01(promise(icl>abstract thing),he) • Tags introduced manually to resolve phrase boundaries – E.g. It delineates <p>the scope of phrases</p> before <p>conversion of the sentence</p>. 8 November 2003 PP attachment problem 17 Related work • • • • Statistical learning methods used Wordnet is used to find relations between words Analysis of corpus is required Not all aspects of problem considered • The hypothesis does not apply to all cases “PP attachments obey the principle of locality” 8 November 2003 PP attachment problem 18 Observations • Prepositions frequency is calculated from British National Corpus • Classified into 2 parts – Simple Preposition – Ambiguous prepositions Frequency Preposition Poly. count 29391 18214 9343 14 16 of in to by way of by means of 7 10 8 1 1 8 November 2003 PP attachment problem 19 Addition to Semantic Attributes hierarchy • Semantic attributes required to disambiguate • Addition required, if existing attributes fail to classify • necessary condition – the attributes should be able to classify the semantically separate structures as separate entities. e.g. the train for Delhi the price for the Hill Road pool 8 November 2003 PP attachment problem ….to() ….mod() 20 Inclusion of preposition in UNL expression • a picture on the wall plc(picture, wall). • The cat walked across the street. – Wrong UNL *plc ( walk, street ) -cat walked along the street -cat walked across the street – Correct UNL plc (walk, :01) obj:01(across, street) 8 November 2003 PP attachment problem 21 Classification based on syntax structure • Sentences have different syntactic structure • Parsing the depends on surface structure - Active-passive, transitive-di-transitive, present-past participles etc. • Classification based on syntax pattern [ Verb + for + Noun phrase] v-pur He was waiting for the rainy day. v-pur He applied for a certificate. [ Noun phrase + for + Noun phrase] n-mod The search for the policy is going on. n-mod He pays the price for his indulgence. 8 November 2003 PP attachment problem 22 Classification based on semantics • Deciding factors – Syntax, attributes, preposition, subcategorisation frame(for verbs) Partial list of preposition on and its possible semantic relation Relation plc ins tim seq mod ins plc 8 November 2003 Example sentence ON a picture on a wall to travel on the bus He came on Sunday Report to reception on arrival a book on South Africa She played a tune on her guitar You can get me on 0181 530 3906 PP attachment problem 23 Updating rule base • Simpler if the classification is perfect. • Issues involved – Priority, proper specification Two rules showing difference in priority – specific to general Comment ;N/abs for N/abs ;search for policy delete preposition for DL(N,ABS) {PRE,#FOR:::} {N,ABS:+PRERES,+FORRES,+pPUR::}P25; ;V FOR N-UNIT-QUARES ;suspend for 2 days Comment Delete preposition for 8 November 2003 DL(VRB){PRE,#FOR:::} {N,UNIT,TIM,QUARES :+PRERES,+FORRES,+pDUR::}P30; PP attachment problem 24 Conclusion • World knowledge is realized in terms of semantic attributes. • Phrasal verbs are not considered • Idiomatic constructs are not handled - e.g. day by day all the time 8 November 2003 PP attachment problem 25