Syntactic Disambiguation through Lexicon Enrichment

1. Syntactic Disambiguation through Lexicon Enrichment Second Stage Project Presentation Guide: Pushpak Bhattacharyya Ashish Almeida 03M05601

2. Overview • Motivation • Problem definition • Linguistic theory • Lexical enrichment • Design and implementation • Results • Future work

3. Motivation • Robust and scalable UNL generation required • English analysis for extracting meaning • Correct analysis  correct meaning representation • Identification of correct syntactic representation • Identification of correct semantic relation

4. Preposition Phrase Attachment Problem • John covered the baby with a blanket. covered covered John the baby with Wrong ! John the baby a blanket with a blanket Verb attachment Noun attachment

5. Universal Networking Language forward(icl>send) @ entry @ past agt gol obj He(icl>person) minister(icl>person) @def mail(icl>collection) @def • He forwarded the mail to the minister.

6. Linguistic Insights • Syntactic level • Syntactic Frame • Subcategorization • Semantic level • Selectional restrictions • Thematic/theta roles

7. Syntactic Frame • Sequence of words as they appear in sentences • [V-ART-N] … handed a book • [NP-to-NP] ... the mail to the minister • [V-NP-P-NP] … forwarded the mail to the minister

8. Subcategorization • Verbs • He put the book on the table. • *He put the book. • *He put. • put: [ _NP PP-on] • Nouns • his reliance on/*at/*with her help. • *his reliance. • reliance: [ _PP-on] • Adjectives • He is fond of reading. • fond: [ _ PP-of]

9. Selectional Restrictions • The boy murdered John. • *The boy murdered the tree. • Thus the verb ‘murder’ needs a human as object. • murder: [HUMAN _ HUMAN]

10. Thematic Roles • Each argument of verb has a unique role associated with it. • Each role is assigned to a single argument. E.g. • The boy murdered John. • The boy - agent • John - patient/theme • Other thematic roles : Instrument, locative, goal • UNL relations: analogous to thematic roles.

11. Lexicon Enrichment • Idiosyncratic information • Subcategorization • Thematic roles in terms of UNL relations • How to get this information ? • Subcategorization • Oxford advanced learner’s dictionary, WordNet • UNL relations • Beth Levin, manual effort

12. An Example Dictionary Entry • forward • E.g. he forwarded the mail to the minister • [forward]{}“forward(icl>sent)” (VRB,VOA,VOA-PHSL, #_TO_A2,#_TO_A2_gol)<E,0,0>; headword Universal Word Attributes

13. Issues • The work focuses on • The [V-NP-P-NP] frame • Commonly used prepositions • In, on, of, with, from, to, for • Disambiguating to • Active voice

14. Design • Fill the valency of the nearest element first. • If in the frame [V-N1-P-N2] • both V and N1 have #P in their subcategorization frames, then satisfy the demand of the nearest element to P, i.e., the noun first. • Else, give priority to that element which subcategorizes the preposition P • Else, give priority to the events and actions (can be verb or noun) • destroyV, destructionN etc.

15. Summarization of Algorithm

16. Problems with to • Infinitival to • Do not allow onion to brown • Preposition to • The lights changed from green to brown Problem: Detect if the lexical element is to-preposition or to-infinitive

17. Heuristics to Detect to-preposition

18. Implementation • Creating new dictionary with extra attributes • Writing new rules to use these new attributes • Rules to use subcategorization information • Rules for processing events (nouns and verbs)

19. Analysis Engine (Enconverter) sentence Word1 Word2 Word3 Word4 Wordn … • Analysis windows • Left Analysis Window (LAW) • Right Analysis Window (RAW) • Condition windows • Many in number • LCWs, RCWs windows LAW RCW RAW LCW

20. Operations in Analysis • Movement of heads • Addition of two nodes • Deletion of a node • Creating relation between two nodes • Adding dynamically inferred attributes to node

21. Rules ; Right shift to affect noun attachment R{VRB,#_FOR_AR2:::}{N,#_FOR:::}(PRE,#FOR)P60; This states that IF The left analysis window is on a verbwhich takes a for-pp as the second argument (indicated by #_FOR_AR2) AND The right analysis window is on a nounwhich takes a for-pp as an argument (indicated by #_FOR) AND The preposition for follows the noun (indicated by (PRE,#FOR) ) THEN Shift right (indicated by R at the start of the rule) anticipating noun attachment for the PP.

22. Other Rules ; Create relation between V and N2, after resolving the preposition preceding N2 <{VRB,#_FOR_AR2,#_FOR_AR2_rsn:::} {N,FORRES,PRERES::rsn:}P25; ;Delete the preposition ON >(VRB,EVENT,VOA){PRE,#ON:::} {N,UNIT,TIME,DAY:+ONRES,+PRERES::}P27; ;Create the relation tim between verb and noun <{VRB,VOA:::} {N,TIME,UNIT,ONRES,PRERES::tim:}P20;

23. Testing • Resources: • British National Corpus • WordNet • Brown corpus • Filtered out • Phrasal verbs • Compound nouns • Longer sentences • Semantically different types of constructs tested in [V-N-P-N] frame.

24. Cases of with Different semantic Roles in different syntactic and semantic environments

25. Results for of-preposition The results of testing for solving PP attachment and generating UNL

26. Conclusion • Lexical enrichment originating from key linguistic principles makes the analysis more correct • Rule-base design simplified due to distinction made between complements and adjuncts during analysis

27. Future Work • Handling the alternation patterns of verbs • Applying the algorithm on all prepositions • Extracting the information through various resources • such as dictionaries and annotated corpus

28. References • UNDL Foundation: The Universal Networking Language (UNL) specifications version 3.2. (2003) http://www.unlc.undl.org • Grimshaw, Jane.: Argument Structure. The MIT Press, Cambridge, Mass. (1990) • Brill, E. and Resnik, R.: A Rule based approach to Prepositional Phrase Attachment disambiguation. Proc. of the fifteenth International conference on computational linguistics. Kyoto. (1994) • Levin, Beth.: English verb Classes and Alternation. The University of Chicago Press, Chicago. (1993) • Hornby, A. S.: Oxford Advanced Learner’s Dictionary of Current English. Oxford University Press, Oxford.(2000)

29. Thank you

30. Example UNL In I deposited my money in my bank account. {unl} gol(deposit(icl>put):02.@entry.@past, account(icl>statement):0W) obj(deposit(icl>put):02.@entry.@past, money(icl>currency):0F) agt(deposit(icl>fasten):02.@entry.@past, I:0C) mod(money(icl>currency):0F, I:0C) mod(account(icl> statement):0W, bank(icl>possession):0R) mod(account(icl> statement):0W, I:0O) {/unl}

31. Example UNL • On • I put the book on the table. • {unl} • gol(put(icl>move):02.@present.@entry, table(icl>object):0M.@def) • obj(put(icl>move):02.@present.@entry, book(icl>publication):0A.@def) • agt(put(icl>move):02.@present.@entry, I:00) • {/unl}

32. Example UNL To They served a wonderful meal to fifty delegates. {unl} gol(serve(icl>provide):05.@entry.@past, delegate(icl>person):12.@pl) obj(serve(icl>provide):05.@entry.@past, meal(icl>food):0O.@indef) agt(serve(icl>provide):05.@entry.@past, they(icl>thing):00) mod(meal(icl>food):0O.@indef, wonderful(mod<thing):0E) qua(delegate(icl>person):12.@pl, fifty(icl>number):0W) {/unl}