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Putting Meaning Into Your Trees

Putting Meaning Into Your Trees. Martha Palmer Paul Kingsbury, Olga Babko-Malaya, Scott Cotton, Nianwen Xue, Shijong Ryu, Ben Snyder PropBanks I and II site visit University of Pennsylvania, October 30, 2003. Powell met Zhu Rongji. battle. wrestle. join. debate.

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Putting Meaning Into Your Trees

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  1. Putting Meaning Into Your Trees Martha Palmer Paul Kingsbury, Olga Babko-Malaya, Scott Cotton, Nianwen Xue, Shijong Ryu, Ben Snyder PropBanks I and II site visit University of Pennsylvania, October 30, 2003

  2. Powell met Zhu Rongji battle wrestle join debate Powell and Zhu Rongji met consult Powell met with Zhu Rongji Proposition:meet(Powell, Zhu Rongji) Powell and Zhu Rongji had a meeting Proposition Bank:From Sentences to Propositions meet(Somebody1, Somebody2) . . . When Powell met Zhu Rongji on Thursday they discussed the return of the spy plane. meet(Powell, Zhu) discuss([Powell, Zhu], return(X, plane))

  3. Capturing semantic roles* • JK broke [ ARG1 the LCD Projector.] • [ARG1 The windows] were broken by the hurricane. • [ARG1 The vase] broke into pieces when it toppled over. SUBJ SUBJ SUBJ *See also Framenet, http://www.icsi.berkeley.edu/~framenet/

  4. Outline • Introduction • Proposition Bank • Starting with Treebanks • Frames files • Annotation process and status • PropBank II • Automatic labelling of semantic roles • Chinese Proposition Bank

  5. (S (NP-SBJ Analysts) • (VP have • (VP been • (VP expecting • (NP (NP a GM-Jaguar pact) • (SBAR (WHNP-1that) • (S (NP-SBJ *T*-1) • (VP would • (VP give • (NP the U.S. car maker) • (NP (NP an eventual (ADJP 30 %) stake) • (PP-LOC in (NP the British company)))))))))))) VP have been VP expecting SBAR NP a GM-Jaguar pact WHNP-1 that VP give NP Analysts have been expecting a GM-Jaguar pact that would give the U.S. car maker an eventual 30% stake in the British company. NP the US car maker NP an eventual 30% stake in the British company A TreeBanked Sentence S VP NP-SBJ Analysts NP S VP NP-SBJ *T*-1 would NP PP-LOC

  6. (S Arg0 (NP-SBJ Analysts) • (VP have • (VP been • (VP expecting • Arg1 (NP (NP a GM-Jaguar pact) • (SBAR (WHNP-1that) • (S Arg0 (NP-SBJ *T*-1) • (VP would • (VP give • Arg2 (NP the U.S. car maker) • Arg1 (NP (NP an eventual (ADJP 30 %) stake) • (PP-LOC in (NP the British company)))))))))))) a GM-Jaguar pact Arg0 that would give Arg1 *T*-1 an eventual 30% stake in the British company Arg2 the US car maker expect(Analysts, GM-J pact) give(GM-J pact, US car maker, 30% stake) The same sentence, PropBanked have been expecting Arg1 Arg0 Analysts

  7. Frames File Example: expect Roles: Arg0: expecter Arg1: thing expected Example: Transitive, active: Portfolio managers expect further declines in interest rates. Arg0: Portfolio managers REL: expect Arg1: further declines in interestrates

  8. Frames File example: give Roles: Arg0: giver Arg1: thing given Arg2: entity given to Example: double object The executives gave the chefsa standing ovation. Arg0: The executives REL: gave Arg2: the chefs Arg1: a standing ovation

  9. Trends in Argument Numbering • Arg0 = agent • Arg1 = direct object / theme / patient • Arg2 = indirect object / benefactive / instrument / attribute / end state • Arg3 = start point / benefactive / instrument / attribute • Arg4 = end point

  10. Ergative/Unaccusative Verbs Roles (no ARG0 for unaccusative verbs) Arg1 = Logical subject, patient, thing rising Arg2 = EXT, amount risen Arg3* = start point Arg4 = end point Sales rose 4% to $3.28 billion from $3.16 billion. The Nasdaq composite index added 1.01 to 456.6 on paltry volume.

  11. Function tags for English/Chinese (arguments or adjuncts?) • Variety of ArgM’s (Arg#>4): • TMP - when? • LOC - where at? • DIR - where to? • MNR - how? • PRP -why? • TPC – topic • PRD -this argument refers to or modifies another • ADV –others • CND – conditional • DGR – degree • FRQ - frequency

  12. Inflection • Verbs also marked for tense/aspect • Passive/Active • Perfect/Progressive • Third singular (is has does was) • Present/Past/Future • Infinitives/Participles/Gerunds/Finites • Modals and negation marked as ArgMs

  13. Word Senses in PropBank • Orders to ignore word sense not feasible for 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 to traditional word senses as in WordNet?

  14. Overlap between Groups and Framesets – 95% Frameset2 Frameset1 WN1 WN2 WN3 WN4 WN6 WN7 WN8 WN5 WN 9 WN10 WN11 WN12 WN13 WN 14 WN19 WN20 develop Palmer, Dang & Fellbaum, NLE 2004

  15. Annotator accuracy – ITA 84%

  16. English PropBank Status - (w/ Paul Kingsbury & Scott Cotton) • Create Frame File for that verb - DONE • 3282 lemmas, 4400+ framesets • First pass: Automatic tagging (Joseph Rosenzweig) • Second pass: Double blind hand correction • 118K predicates – all but 300 done • Third pass: Solomonization (adjudication) • Betsy Klipple, Olga Babko-Malaya – 400 left • Frameset tags • 700+, double blind, almost adjudicated, 92% ITA • Quality Control and general cleanup

  17. Quality Control and General Cleanup • Frame File consistency checking • Coordination with NYU • Insuring compatibility of frames and format • Leftover tasks • have, be, become • Adjectival usages • General cleanup • Tense tagging • Finalizing treatment of split arguments, ex. say, and symmetric arguments, ex. match • Supplementing sparse data w/ Brown for selected verbs

  18. Summary of English PropBankPaul Kingsbury, Olga Babko-Malaya, Scott Cotton

  19. PropBank II • Nominalizations NYU • Lexical Frames DONE • Event Variables, (including temporals and locatives) • More fine-grained sense tagging • Tagging nominalizations w/ WordNet sense • Selected verbs and nouns • Nominal Coreference • not names • Clausal Discourse connectives – selected subset

  20. sense tags; discourse connectives { } help2,5 tax rate1 keep1 company1 PropBank I I Also, [Arg0substantially lower Dutch corporate tax rates] helped [Arg1[Arg0 the company] keep [Arg1 its tax outlay] [Arg3-PRD flat] [ArgM-ADV relative to earnings growth]]. Event variables; nominal reference; REL Arg0 Arg1 Arg3-PRD ArgM-ADV help tax rates the company keep its tax outlay flat keep the company its tax outlay flat relative to earnings…

  21. Summary of Multilingual TreeBanks, PropBanks * Also 1M word English monolingual PropBank

  22. Agenda • PropBank I 10:30 – 10:50 • Automatic labeling of semantic roles • Chinese Proposition Bank • Proposition Bank II 10:50 – 11:30 • Event variables – Olga Babko Malaya • Sense tagging – Hoa Dang • Nominal coreference – Edward Loper • Discourse tagging – Aravind Joshi • Research Areas – 11:30 – 12:00 • Moving forward – Mitch Marcus • Alignment improvement via dependency structures– Yuan Ding • Employing syntactic features in MT – Libin Shen • Lunch 12:00 – 1:30 White Dog • Research Area - 1:30 – 1:45 • Clustering – Paul Kingsbury • DOD Program presentation – 1:45 – 2:15 • Discussion 2:15 – 3:00

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