Grouping improved scores: ITA 82%, MaxEnt WSD 69% WordNet: – call, 28 senses, Senseval2 groups (engineering!) WordNet: – call, 28 senses

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Grouping improved scores: ITA 82%, MaxEnt WSD 69% WordNet: – call, 28 senses, Senseval2 groups (engineering!) WordNet: – call, 28 senses

Boston University, US has reference to this Academic Journal, Hierarchical Sense Distinctions Martha Palmer University of Pennsylvania alongside Olga Babko-Malaya, Nianwen Xue, in addition to Ben Snyder July 25, 2004 Senseval3 Workshop ? ACL-04 Outline Granularity of sense distinctions Propbanks Hierarchical sense distinctions Lessons learned Moving forward WordNet ? Princeton (Miller 1985, Fellbaum 1998) On-line lexical reference (dictionary) Nouns, verbs, adjectives, in addition to adverbs grouped into synonym sets Other relations include hypernyms (ISA), antonyms, meronyms Typical top nodes – 5 out of 25 (act, action, activity) (animal, fauna) (artifact) (attribute, property) (body, corpus)

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WordNet ? Princeton (Miller 1985, Fellbaum 1998) Limitations as a computational lexicon Contains little syntactic information Comlex has syntax but no sense distinctions No explicit lists of predicate arguments Sense distinctions very fine-grained, Definitions often vague Causes problems alongside creating training data in consideration of supervised Machine Learning ? SENSEVAL2 29 Verbs > 16 senses (including call) Inter-annotator Agreement ITA 73%, Automatic Word Sense Disambiguation, WSD 60.2% Slow annotation speed ? 60 tokens per hour Dang & Palmer, SIGLEX02 WordNet ? call, 28 senses name, call (assign a specified, proper name to; “They named their son David”; ?) -> LABEL 2. call, telephone, call up, phone, ring (get or try so that get into communication (with someone) by telephone; “I tried so that call you all night”; ?) ->TELECOMMUNICATE 3. call (ascribe a quality so that or give a name of a common noun that reflects a quality; “He called me a bastard”; ?) -> LABEL 4. call, send in consideration of (order, request, or command so that come; “She was called into the director’s office”; “Call the police!”) -> ORDER WordNet: – call, 28 senses WN2 , WN13,WN28 WN15 WN26 WN3 WN19 WN4 WN 7 WN8 WN9 WN1 WN22 WN20 WN25 WN18 WN27 WN5 WN 16 WN6 WN23 WN12 WN17 , WN 11 WN10, WN14, WN21, WN24

WordNet: – call, 28 senses, Senseval2 groups (engineering!) WN5, WN16,WN12 WN15 WN26 WN3 WN19 WN4 WN 7 WN8 WN9 WN1 WN22 WN20 WN25 WN18 WN27 WN2 WN 13 WN6 WN23 WN28 WN17 , WN 11 WN10, WN14, WN21, WN24, Loud cry Label Phone/radio Bird or animal cry Request Call a loan/bond Visit Challenge Bid Grouping improved scores: ITA 82%, MaxEnt WSD 69% Call: 31% of errors due so that confusion between senses within same group 1: name, call (assign a specified, proper name to; They named their son David) call (ascribe a quality so that or give a name of a common noun that reflects a quality; He called me a bastard) call (consider or regard as being;I would not call her beautiful) 75% alongside training in addition to testing on grouped senses vs. 43% alongside training in addition to testing on fine-grained senses Palmer, Dang, Fellbaum,, submitted, NLE Proposition Bank: From Sentences so that Propositions (Predicates!) . . . When Powell met Zhu Rongji on Thursday they discussed the return of the spy plane. meet(Powell, Zhu) discuss([Powell, Zhu], return(X, plane)) meet(Somebody1, Somebody2)

In Pattern Matching The End

A TreeBanked phrase 1M words WSJ ? Penn TreeBank II S VP would VP give NP PP-LOC A GM-Jaguar pact would give the U.S. car maker an eventual 30% stake in the British company. The same phrase, PropBanked Same data ? released, March?04 would give the US car maker an eventual 30% stake in the British company Arg0 Arg2 Arg1 A GM-Jaguar pact would give the U.S. car maker an eventual 30% stake in the British company. Frames File example: give < 4000 Frames in consideration of PropBank Roles: Arg0: giver Arg1: thing given Arg2: entity given so that Example: double object The executives gave the chefs a standing ovation. Arg0: The executives REL: gave Arg2: the chefs Arg1: a standing ovation Frames File example: give Roles: Arg0: giver Arg1: thing given Arg2: entity given so that Example: double object The executives gave the chefs a standing ovation. Arg0: Agent The executives REL: gave Arg2: Recipient the chefs Arg1: Theme a standing ovation VerbNet ? based on Levin classes Word Senses in PropBank Orders so that ignore word sense not feasible in consideration of 700+ verbs Mary left the room Mary left her daughter-in-law her pearls in her will Frameset leave.01 "move away from": Arg0: entity leaving Arg1: place left Frameset leave.02 "give": Arg0: giver Arg1: thing given Arg2: beneficiary How do these relate so that traditional word senses in WordNet? WordNet: - call, 28 senses, groups WN5, WN16,WN12 WN15 WN26 WN3 WN19 WN4 WN 7 WN8 WN9 WN1 WN22 WN20 WN25 WN18 WN27 WN2 WN 13 WN6 WN23 WN28 WN17 , WN 11 WN10, WN14, WN21, WN24, Loud cry Label Phone/radio Bird or animal cry Request Call a loan/bond Visit Challenge Bid Overlap alongside PropBank Framesets WN5, WN16,WN12 WN15 WN26 WN3 WN19 WN4 WN 7 WN8 WN9 WN1 WN22 WN20 WN25 WN18 WN27 WN2 WN 13 WN6 WN23 WN28 WN17 , WN 11 WN10, WN14, WN21, WN24, Loud cry Label Phone/radio Bird or animal cry Request Call a loan/bond Visit Challenge Bid Overlap between Senseval2 Groups in addition to Framesets ? 95% WN1 WN2 WN3 WN4 WN6 WN7 WN8 WN5 WN 9 WN10 WN11 WN12 WN13 WN 14 WN19 WN20 Frameset1 Frameset2 develop Sense Hierarchy (Palmer, et al, SNLU04 - NAACL04) PropBank Framesets ? ITA >90% coarse grained distinctions 20 Senseval2 verbs w/ > 1 Frameset Maxent WSD system, 73.5% baseline, 90% accuracy Sense Groups (Senseval-2) – ITA 82% Intermediate level (includes Levin classes) ? 69% WordNet ? ITA 71% fine grained distinctions, 60.2%

Summary of English/Chinese TreeBanks, PropBanks Lessons Learned Desiderata Balanced corpora alongside high quality annotation Issues Annotation process requires speed in addition to accuracy What is ?semantic? accuracy? ITA? Compromises that enable speed in addition to ITA PropBank requires frames files, Arg descriptions Sense tagging requires coarser granularity Are they useful? Are they useful enough? PropBank I Also, [Arg0substantially lower Dutch corporate tax rates] helped [Arg1[Arg0 the company] keep [Arg1 its tax outlay] [Arg3-PRD flat] [ArgM-ADV relative so that earnings growth]]. relative so that earnings? flat its tax outlay the company keep the company keep its tax outlay flat tax rates help ArgM-ADV Arg3-PRD Arg1 Arg0 REL Event variables; nominal reference; I

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