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Towards Semantics Generation

Towards Semantics Generation. Third stage presentation of M.S project Ashish Almeida 03M05601 Guide Prof. Pushpak Bhattacharyya. Motivation. Goal: semantic role labeling To commonly used functional element in English. (34% (source: Penn tree-bank))

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Towards Semantics Generation

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  1. Towards Semantics Generation Third stage presentation of M.S project Ashish Almeida 03M05601 Guide Prof. Pushpak Bhattacharyya

  2. Motivation • Goal: semantic role labeling • To commonly used functional element in English. (34% (source: Penn tree-bank)) • To act as both preposition and as infinitival marker. • PRO was not considered before in semantic labeling

  3. Roadmap • Problem • UNL* • Linguistic analysis • Attachment solution • Dictionary creation • Implementation • Conclusion

  4. Current work (third stage) • Organization of attributes • Analysis of to-infinitive • PRO-handling and resolution • Acquisition of attributes for dictionary

  5. Problem • Semantics generation for sentences involving lexeme to • Three problems • Identifying the proper part of speech (POS) • Attachment ambiguity resolution • Handling PRO • Focus Only [V-N-to-N/V] frames considered. Document specific dictionary used

  6. UNL* give(icl>do) @entry.@past • UNL • UWs • Relations gol agt obj John(icl>person) Mary(iof>person) flower(icl>flora)

  7. Differentiating POS • Identify to-preposition phrase from to-infinitival clause • … gave papers to the judge - to is followed by a determiner • … increases to 25 million rupees - to is followed by a number • … to cooks. - to is followed by a plural noun

  8. Differentiating POS … to-infinitival • …to go… - to is followed by a base verb • … to clearly write… - to is followed by adverb followed by base verb.

  9. Attachment algorithm For Prepositional phrases

  10. Example • John gave a flower to Mary. • Verb gave expects to • Noun flowerdoes not expect to • Apply case 3 • Attach ‘to Mary’ to gave • Final UNL:

  11. To infinitival clauses • Example 1a. He promised me [to come for the party]. 1b. Heipromised me [PROito come for the party]. promise subject controlled pro 2a. They forced Mary [to give a party]. 2b. They forced Maryj[PROj to give a party]. force object controlled pro

  12. UNL representation Theyi promised Mary [PROi to give a party].

  13. Attachment algorithm table for to-infinitival clauses

  14. PRO resolution Example a. He ordered us [to finish the work]. b. He ordered usi [PROi to finish the work]. Steps • fetch PRO type fom dictionary entry of order • Resolve all relations within clause - [PROi to finish the work] • Relate the clause to verb order • Finally replace the PRO with actual UW

  15. Semantic relations • Filled using the Levin’s verb classes. • No semantically role resource available • Stored in dictionary along with argument information

  16. System Sentence having to Detect part of speech To-infinitive To-preposition Find attachment site Decide type and existence of PRO Find attachment site Resolve pro Find semantic relation Find semantic relation Coindex the PRO UNL expressions

  17. Dictionary • All words must be present in dictionary • Structure [letter] “letter(icl>document)” (N,INANI,PHSCL) <E,0,0> headword Universal word Attributes

  18. Dictionary: Acquisition of attributes New attribute needed to apply the algorithm • Argument structure information • Semantic relations • PRO control property of verbs • Oxford, WordNet • Penn Treebank • Beth Levin’s verb classification

  19. from WordNet • Sentence frames for verbs • Example • For verb want • They ____ him to write the letter. For the verb promise • Somebody ----s somebody to INFINITIVE

  20. from Oxford dictionary • Oxford advanced learners dictionary (OALD) provides partial frames wherever applicable • Examples effort noun …… 2 [C] ~ (to do sth)an attempt to do sth especially when it is difficult to do: to make a determined / real / special effort to finish on time ….. force verb make sb do sth 1 [often passive] ~ sb (into sth / into doing sth) to make sb do sth that they do not want to do SYN COMPEL …• [VN to inf]I was forced to take a taxi because the last bus had left. • She forced herself to be polite to them. …

  21. from Penn Treebank • Syntactically annotated corpus • Example • Algorithm to extract this property

  22. Organizing attributes • WordNet noun ontology explored. • The top level labels used as attributes. • Example:

  23. English to UNL system Partial UNL expression • Rule base UNL expression Input sentence Enconverter Post editor WordNet OALD Penn tree-bank

  24. Implementation • POS Identification • Finding Attachment site • Creating Relation • PRO insertion • Post processing • Resolve the co-reference.

  25. Identification of POS Pattern to detect to infinitive: -to followed by verb in base form :{:::}{^TO_INF_NEXT:+TO_INF_NEXT::}(#TO,TO_INF)(BLK)(VRB,V_1)P40; IF (The left analysis window (indicated by {}) is on any word) AND (The right analysis window is on a word which does not have a TO_INF_NEXT i.e. look ahead is not performed yet. ) THEN Select the next sequence of words such that they will satisfy the conditions as – pick the word to corresponding to infinitival-to (indicated by attributes #TO and TO_INF) AND pick a space (indicated as BLK) AND pick a verb which is in its simple form (indicated by V_1) AND add the property TO_INF_NEXT to the word in the right analysis window

  26. Attachment rules • Do noun attachment • Move ahead when on frame [V][N]-P-N R{VRB,#_TO_AR2:::}{N,#_TO:::}(PRE,#TO)P60; • Create goal relation • gol(uw1, uw2) <{VRB,#_TO_AR2,#_TO_AR2_gol:::}{N,TORES,PRERES::gol:}P25;

  27. Handling PRO • Produce a “PRO” element in UNL with appropriate relation. (Enconverter) :{VRB,SUB_PRO:::}"[[SUB_PRO]]:N,SUB_PRO, #INSERTED::"(VRB,TO_INFRES,^PRORES)P30; 2. Relate it to the verb of the infinitive clause semantically. (Enconverter) >(VRB){N,SUB_PRO::agt:}{VRB,VOA,TO_INFRES: +PRORES,+SUB_PRORES::}P40; 3. Substitute a referred UW in the place of PRO. (Post editor)

  28. Replace PRO Example They promised Maryi [PROito give a party]. agt (promise(icl>do).@entry.@past, they:0A) gol (promise(icl>do).@entry.@past, Mary(iof>person)) obj (promise(icl>do), :01) agt :01(give(icl>do), sub_PRO:0C) obj :01(give(icl>do), party(icl>function)) After post processing agt :01(give(icl>do), they:0A)

  29. Evaluation • Preparation of test sentences • Source : Penn Treebank, edict concordencer and Oxford • Dictionary • Automatic dictionary generator • Editing and corrections • Appending extra attributes.

  30. Results

  31. Conclusion • Automatic acquisition of attributes is effective. • Correct Semantic representation is crucial. • Helps in applications like information retrival, generation to other language, question answering

  32. References • Grimshaw, Jane: Argument Structure. The MIT Press, Cambridge, Mass. (1990) • Mohanty R.K., Almeida A., Srinivas S., Bhattacharyaa P.: The complexity of OF, ICON, Hydrabad, India. (2004) • UNDL Foundation: The Universal Networking Language (UNL) specifications version 3.2. (2003) http://www.unlc.undl.org • Resources • OALD • WordNet • Penn Tree bank • DDG • Concordance search on Brown corpus • Beth Levin’s verb classes

  33. ! Thank you

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