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Prepositional Phrase Attachment Problem

Prepositional Phrase Attachment Problem. 03M05601 Ashish Almeida. Overview. Introduction to NLP Analysis in UNL system Prepositional phrase attachment problem Proposed method to handle this problem. Motivation. Analysis involves many complex problems

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Prepositional Phrase Attachment Problem

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  1. Prepositional Phrase Attachment Problem 03M05601 Ashish Almeida PP attachment problem

  2. Overview • Introduction to NLP • Analysis in UNL system • Prepositional phrase attachment problem • Proposed method to handle this problem PP attachment problem

  3. 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 PP attachment problem

  4. NL understanding Text Meaning NL generation Tasks involved in NLP Analysis and generation PP attachment problem

  5. Phases in NLP • Morphological analysis • Syntactic analysis • Semantic analysis • Discourse integration • Pragmatic analysis PP attachment problem

  6. Is NL Compositional ? • Compsitional expression • Meaning of the whole from meaning of parts e.g. strong tea -rich tea day by day - all the time PP attachment problem

  7. Analysis Morphological + Syntactic + Semantic analysis • All these phases are dependent on each other. • Interactive Vs modular approach • Analysis in UNL system - interactive PP attachment problem

  8. UNL … • UNL is Interlingua e.g. Ram ate rice with spoon. eat(icl>do) @ entry @ present agt ins obj John(iof>person) spoon(icl>artifact) rice(icl>food) PP attachment problem

  9. 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 UWs Attributes PP attachment problem

  10. 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. PP attachment problem

  11. Prepositional Phrase Attachment Problem • Type of Structural ambiguity in a sentence Verb attachment JohnNP readVP the reportNP on new technologies.PP Noun attachment PP attachment problem

  12. read read John the report John on the report on new technologies new technologies Prepositional Phrase Attachment Problem… • Noun attachment Vs verb attachment e.g. John read the report on new technologies. * PP attachment problem

  13. Establishing semantic relation Same structure-different semantic relation e.g. 1. Ram ate rice withspoon.……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)) PP attachment problem

  14. 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 PP attachment problem

  15. Different sites of attachment • The searchforthe policy is going on. • The test will be held at the endofAugust. • InAugust 1947, India became free from British rule. • Wilson received a medal from the commanding officerata farewell party. • There is no restriction on how far the PP can lie from the word to which it relates. PP attachment problem

  16. Affinity with preceding phrase • The preposition ofgets 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. PP attachment problem

  17. 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>. PP attachment problem

  18. 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” PP attachment problem

  19. Frequency Preposition Poly. count 29391 of 7 18214 in 10 9343 to 8 14 by way of 1 16 by means of 1 Observations • Prepositions frequency is calculated from British National Corpus • Classified into 2 parts • Simple Preposition • Ambiguous prepositions PP attachment problem

  20. 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 ….to() the price for the Hill Road pool ….mod() PP attachment problem

  21. 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) PP attachment problem

  22. [ 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. 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 PP attachment problem

  23. Relation Example sentence ON plc a picture on a wall ins to travel on the bus tim He came on Sunday seq Report to reception on arrival mod a book on South Africa ins She played a tune on her guitar plc You can get me on 0181 530 3906 Classification based on semantics • Deciding factors • Syntax, attributes, preposition, subcategorisation frame(for verbs) Partial list of preposition on and its possible semantic relation PP attachment problem

  24. Comment ;N/abs for N/abs ;search for policy delete preposition for DL(N,ABS) {PRE,#FOR:::} {N,ABS:+PRERES,+FORRES,+pPUR::}P25; Comment ;V FOR N-UNIT-QUARES ;suspend for 2 days Delete preposition for DL(VRB){PRE,#FOR:::} {N,UNIT,TIM,QUARES :+PRERES,+FORRES,+pDUR::}P30; Updating rule base • Simpler if the classification is perfect. • Issues involved • Priority, proper specification Two rules showing difference in priority – specific to general PP attachment problem

  25. 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 PP attachment problem

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