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Preposition Phrase Attachment in English Language Analysis

Preposition Phrase Attachment in English Language Analysis. Ashish Almeida 03M05601. read. read. *. John. the report. John. on. the report. on. new technologies. new technologies. PP attached to VP. PP attached to NP. PP attachment. John read the report on new technologies.

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Preposition Phrase Attachment in English Language Analysis

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  1. Preposition Phrase Attachment in English Language Analysis Ashish Almeida 03M05601

  2. read read * John the report John on the report on new technologies new technologies PP attached to VP PP attached to NP PP attachment • John read the report on new technologies. CFILT,IIT Bombay

  3. Same Structure: different roles • Ram ate rice with a spoon.-instrument ins(eat(icl>do).@past.@entry, spoon(icl>tool)) • Ram ate rice with Sita.-co-agent cag(eat(icl>do).@past.@entry, Sita(iof>person)) CFILT,IIT Bombay

  4. UNL and EnConvertor • UNL is an intermediate language for representing meaning of natural language. • EnConvertor is a language independent parser • Rules are written for analysis • UW Dictionary is created for analysis • PP-attachment is handled in EnCo. CFILT,IIT Bombay

  5. More about UNL UNL graph ofJohn eats rice with a spoon eat(icl>do) ins spoon(icl>artifact) @ entry. @ present obj rice(icl>food) agt John(iof>person) CFILT,IIT Bombay

  6. Adjuncts • It provides extra information in a sentence. • It attaches to the verb. Examples: • Ram came home. Ram came home on Monday. • Sita is sleeping. Sita is sleeping in the room. CFILT,IIT Bombay

  7. Temporal Prepositional Phrases • Represents temporal information with the help of an object e.g. on Sunday , for two days, at 5 p.m., on Diwali, in ice age, after the meeting, till 6 O’ clock, over two hours, beyond midnight CFILT,IIT Bombay

  8. Time attributes • To identify the type of time word e.g. CFILT,IIT Bombay

  9. UNL of Temporal PPs Different UNL generation in two different cases • Delete the preposition e.g. come at noon tim(come , noon) • Retain the preposition e.g. come before noon tim(come, before) obj(before,noon) CFILT,IIT Bombay

  10. Mapping from Prepositions to UNL relations CFILT,IIT Bombay

  11. Rules • Rules for at-PP - Applies to “at 6 pm” ;delete at DL(VRB){PRE,#AT:::}{TIME,TIM_TOKEN: +ATRES,+PRERES,+pTIM::}P22; ;create relation tim <{VRB:::}{ATRES,PRERES,pTIM::tim:}P20; CFILT,IIT Bombay

  12. Testing • Wall Street Journal (WSJ) corpus is used. • Sentences from Oxford advanced learner’s Dictionary are also tested. • WSJ has V-N-P-N four-word sentence fragments • All temporal cases are tested for correctness of UNL CFILT,IIT Bombay

  13. Results • Errors are mainly due to • mistakes in corpus and • inaccuracies in UW • dictionary CFILT,IIT Bombay

  14. Associative of-PP • NP: the comedy of Shakespeare mod(comedy(icl>abstract thing), Shakespeare(iof >person)) • NP: the eyes of the boy pof(boy(icl>person), eye(pof>body).@pl) • AP: guilty of an offence obj(guilty(aoj>thing), offence(icl>abstract thing)) • NP: book of Ram pos(book(icl>concrete thing), Ram(iof>person)) CFILT,IIT Bombay

  15. Partitive of-PP • Here, in case of N1-OF-N2 the semantic head is the N2. The first NP indicates a quantity Examples • cup of tea qua(tea, cup) • bag of oranges • bundle of sticks • a pinch of salt • Kind construction e.g. that kind of people mod(people, kind) • Kind-type words : kind, type, sort, varietyetc. CFILT,IIT Bombay

  16. Argument structure (AS) • Argument structure specify the structural frame into which a verb can be fitted. For example, *Ram saw. This is unacceptable as verb see has strict subcategorisation feature (+ _NP). That is verb see takes NP as object. Thus a valid sentence is Ram saw Sita. • AS of see is (NP _ NP) CFILT,IIT Bombay

  17. Adjunct and Complements • He gave a book to Ram. give (NP _ NP to-PP) - without to Ram sentence is unacceptable - to Ram is complement • He gave a book to Ramon Sunday - without on Sunday sentence is acceptable - on Sunday is adjunct • Similarly, nouns and adjectives take complements CFILT,IIT Bombay

  18. More on AS • Ram accused Sita of cheating • AS is (NP _ NP of-PP) • UNL for the sentence frame with the verb accuse (agtNP _ objNPrsnof-PP) • Whereas for adjuncts, the case relations differ. • He saw the girl through the window. • He saw the girl with anger. • He saw the girl in the library. CFILT,IIT Bombay

  19. Dictionary entry He gave a book to Ram. • The lexicon will have entry of gave which provides AS information. [gave] {} “give(icl>do)” (VRB,VOA,VOA-PHSL, #_TO, #_TO_GOL,PAST) <E,0,0>; • PP in the sentence can be identified as a complement or not. • This solves the attachment in some cases. CFILT,IIT Bombay

  20. V NP1 NP2 V attaches to NP1 V attaches to NP2 (A) V V NP1 NP1 NP2 NP2 V attaches to NP2 NP2 attaches to NP1 (C) V attaches to NP1 NP1 attaches to NP2 (B) Verb attachment of ‘of-PP’ • Three possible cases CFILT,IIT Bombay

  21. ... remind him of Gita ... saw the book of physics ... drank a cup of milk of-PP attachment cases Case A Case B Case C CFILT,IIT Bombay

  22. Four cases of attachment • Attachment in presence or absence of attribute of CFILT,IIT Bombay

  23. Rules ;Noun attachment R{VRB,#_OF:::}{N,#_OF:::}(PRE,#OF)P60; ;Noun attachment R{VRB,^#_OF:::}{N:::}(PRE,#OF)P60; ;Verb attachment, first resolve the immediate object <{VRB,#_OF,#_OF_OBJ:::}{N,^#_OF::obj:} (PRE,#OF)P30; CFILT,IIT Bombay

  24. Testing process • British National Corpus (BNC) is used. • V-N-of-N type sentences are extracted from corpus. • AS information is added to verbs and nouns using Oxford Advanced Learner’s Dictionary and Beth Levin’s verb classes. • AS information is merged into the Dictionary. CFILT,IIT Bombay

  25. Results CFILT,IIT Bombay

  26. To sum up • The PP attachment problem is successfully divided in two parts – complement PPs and adjunct PPs • This division clearly delineates the problem so that their analysis does not conflict. • Analysis of complements will be mostly driven by rich attribute set from lexicon • Whereas analysis of adjuncts will be driven by rules CFILT,IIT Bombay

  27. Contribution • Deciding the overall strategy of analysis • Providing computation insights in linguistic analysis • Design of attribute set/introducing new attributes • Design and implementation of rules for EnCo. • Testing of sentences. • Dictionary corrections/modifications CFILT,IIT Bombay

  28. Future work • Focus will be on complements. • Also, that-clause, to and -ing infinite clause handling, PRO detection will be tried in similar fashion. • Automatic/semi-automatic acquisition of AS information for dictionaries will be tried out. CFILT,IIT Bombay

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