1. 10/9/01PropBank1 Proposition Bank: a resource of predicate-argument relations Martha Palmer University of Pennsylvania October 9, 2001 Columbia University

2. 10/9/01PropBank2 Outline  Overview ( Ace consensus: BBN,NYU,MITRE,Penn)  Motivation  Approach Guidelines, lexical resources, frame sets Tagging process, hand correction of automatic tagging  Status: accuracy, progress  Colleagues: Joseph Rosenzweig, Paul Kingsbury, Hoa Dang, Karin Kipper, Scott Cotton, Laren Delfs, Christiane Fellbaum

3. 10/9/01PropBank3 Proposition Bank: Generalizing from Sentences to Propositions Powell met Zhu Rongji Proposition: meet(Powell, Zhu Rongji ) Powell met with Zhu Rongji Powell and Zhu Rongji met Powell and Zhu Rongji had a meeting... When Powell met Zhu Rongji on Thursday they discussed the return of the spy plane. meet(Powell, Zhu) discuss([Powell, Zhu], return(X, plane)) debate consult join wrestle battle meet(Somebody1, Somebody2)

4. 10/9/01PropBank4 Penn English Treebank  1.3 million words  Wall Street Journal and other sources  Tagged with Part-of-Speech  Syntactically Parsed  Widely used in NLP community  Available from Linguistic Data Consortium

5. 10/9/01PropBank5 A TreeBanked Sentence Analysts S NP-SBJ VP have VPbeenVP expecting NP a GM-Jaguar pact NP that SBAR WHNP-1 *T*-1 S NP-SBJ VP would VP give the US car maker NP an eventual 30% stake NP the British company NP PP-LOC in (S (NP-SBJ Analysts) (VP have (VP been (VP expecting (NP (NP a GM-Jaguar pact) (SBAR (WHNP-1 that) (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)))))))))))) Analysts have been expecting a GM-Jaguar pact that would give the U.S. car maker an eventual 30% stake in the British company.

6. 10/9/01PropBank6 The same sentence, PropBanked Analysts have been expecting a GM-Jaguar pact Arg0 Arg1 (S Arg0 (NP-SBJ Analysts) (VP have (VP been (VP expecting Arg1 (NP (NP a GM-Jaguar pact) (SBAR (WHNP-1 that) (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)))))))))))) that would give *T*-1 the US car maker an eventual 30% stake in the British company Arg0 Arg2 Arg1 expect(Analysts, GM-J pact) give(GM-J pact, US car maker, 30% stake)

7. 10/9/01PropBank7 Motivation  Why do we need accurate predicate-argument relations?  They have a major impact on Information Processing.  Ex: Korean/English Machine Translation: ARL/SBIR CoGenTex, Penn, Systran (K/E Bilinugal Lexicon, 20K) 4K words ( < 500 words from Systran, military messages) Plug and play architecture based on DsyntS (rich dependency structure) Converter bug led to random relabeling of predicate arguments Correction of predicate argument labels alone led to tripling of acceptable sentence output

8. 10/9/01PropBank8 Focusing on Parser comparisons  200 sentences hand selected to represent “good” translations given a correct parse.  Used to compare: Corrected DsyntS output Juntae’s parser output (off-the-shelf) Anoop’s parser output (Treebank trained, 95% F)

9. 10/9/01PropBank9 Evaluating translation quality  Compare DLI Human translation to system output (200)  Criteria used by human judges (2 or more, not blind) [g] = good, exactly right [f1] = fairly good, but small grammatical mistakes [f2] = Needs fixing, but vocabulary basically there [f3] = Needs quite a bit of fixing, usually some un-translated vocabulary, but most v. is right [m] = seems grammatical, but semantically wrong, actually misleading [i] = irredeemable, really wrong, major problems

10. 10/9/01PropBank10 Results Comparison = 200 sent.

11. 10/9/01PropBank11 Plug and play?  Converter used to map Parser outputs into MT DsyntS format Bug in the converter affected both systems Predicate argument structure labels were being lost in the conversion process, relabeled randomly  The converter was also still tuned to Juntae’s parse output, needed to be customized to Anoop’s

12. 10/9/01PropBank12 Anoop’s parse -> MTW DsyntS –0010Target: Unit designations are normally transmitted in code. –0010Corrected: Normally unit designations are notified in the code. –0010Anoop: Normally it is notified unit designations in code. notified unit normally code designations C = Arg1 P = Arg0

13. 10/9/01PropBank13 Anoop’s parse -> MTW DsyntS 0022Target: Under what circumstances does radio inteference occur? 0022Corrected: In what circumstances does the interference happen in the radio? 0022Anoop: Do in what circumstance happen interference in radio? happen what radio interference circumstances C = Arg0 P = ArgM C = Arg1 P = Arg0

14. 10/9/01PropBank14 New and Old Results Comparison

15. 10/9/01PropBank15 English PropBank  1M words of Treebank over 2 years, May’01-03  New semantic augmentations Predicate-argument relations for verbs label arguments: Arg0, Arg1, Arg2, … First subtask, 300K word financial subcorpus (12K sentences, 35K+ predicates)  Spin-off: Guidelines (necessary for annotators) English lexical resource 6000+ verbs with labeled examples, rich semantics

16. 10/9/01PropBank16 Task: not just undoing passives  The earthquake shook the building.  The walls shook; the building rocked. ;  The guidelines = lexicon with examples: Frames Files

17. 10/9/01PropBank17 Guidelines: Frames Files  Created manually – Paul Kingsbury working on semi-automatic expansion  Refer to VerbNet, WordNet and Framenet  Currently in place for 230 verbs Can expand to 2000+ using VerbNet Will need hand correction  Use “semantic role glosses” unique to each verb (map to Arg0, Arg1 labels appropriate to class)

18. 10/9/01PropBank18 Frames 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 interest rates

19. 10/9/01PropBank19 Frames File example: give Roles: Arg0: giver Arg1: thing given Arg2: entity given to Example: double object The executives gave the chefs a standing ovation. Arg0: The executives REL: gave Arg2: the chefs Arg1: a standing ovation

20. 10/9/01PropBank20 The same sentence, PropBanked Analysts have been expecting a GM-Jaguar pact Arg0 Arg1 (S Arg0 (NP-SBJ Analysts) (VP have (VP been (VP expecting Arg1 (NP (NP a GM-Jaguar pact) (SBAR (WHNP-1 that) (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)))))))))))) that would give *T*-1 the US car maker an eventual 30% stake in the British company Arg0 Arg2 Arg1 expect(Analysts, GM-J pact) give(GM-J pact, US car maker, 30% stake)

21. 10/9/01PropBank21 Complete Sentence Analysts have been expecting a GM-Jaguar pact that *T*-1 would give the U.S. car maker an eventual 30% stake in the British company and create joint ventures that *T*-2 would produce an executive-model range of cars.

22. 10/9/01PropBank22 How are arguments numbered?  Examination of example sentences  Determination of required / highly preferred elements  Sequential numbering, Arg0 is typical first argument, except Oergative/unaccusative verbs (shake example) OArguments mapped for "synonymous" verbs

23. 10/9/01PropBank23 Additional tags (arguments or adjuncts?)  Variety of ArgM’s (Arg#>4): TMP - when? LOC - where at? DIR - where to? MNR - how? PRP -why? REC - himself, themselves, each other PRD -this argument refers to or modifies another ADV -others

24. 10/9/01PropBank24 Tense/aspect  Verbs also marked for tense/aspect OPassive OPerfect OProgressive OInfinitival  Modals and negation marked as ArgMs

25. 10/9/01PropBank25 Ergative/Unaccusative Verbs: rise Roles 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. *Note: Have to mention prep explicitly, Arg3-from, Arg4-to, or could have used ArgM-Source, ArgM-Goal. Arbitrary distinction.

26. 10/9/01PropBank26 Synonymous Verbs: add in sense rise Roles: Arg1 = Logical subject, patient, thing rising/gaining/being added to Arg2 = EXT, amount risen Arg4 = end point The Nasdaq composite index added 1.01 to 456.6 on paltry volume.

27. 10/9/01PropBank27 Phrasal Verbs  Put together  Put in  Put off  Put on  Put out  Put up ...

28. 10/9/01PropBank28 Frames: Multiple Rolesets  Rolesets are not necessarily consistent between different senses of the same verb Verb with multiple senses can have multiple frames, but not necessarily  Roles and mappings onto argument labels are consistent between different verbs that share similar argument structures, Similar to Framenet Levin / VerbNet classes http://www.cis.upenn.edu/~dgildea/VerbNet/  Out of the 179 most frequent verbs: 1 Roleset – 92 2 rolesets – 45 3+ rolesets – 42 (includes light verbs)

29. 10/9/01PropBank29 Annotation procedure  Extraction of all sentences with given verb  First pass – automatic tagging  Second pass: Double blind hand correction Variety of backgrounds less syntactic training than for treebanking  Script to discover discrepancies  Third pass: Solomonization (adjudication)

30. 10/9/01PropBank30 Inter-annotator agreement

31. 10/9/01PropBank31 Annotator Accuracy vs. Gold Standard  One version of annotation chosen (sr. annotator)  Solomon modifies => Gold Standard

32. 10/9/01PropBank32 Status  179 verbs framed (+ Senseval2 verbs)  97 verbs first-passed O12,300+ predicates ODoes not include ~3000 predicates tagged for Senseval  54 verbs second-passed O6600+ predicates  9 verbs solomonized O885 predicates

33. 10/9/01PropBank33 Throughput  Framing: approximately 2 verbs per hour  Annotation: approximately 50 sentences per hour  Solomonization: approximately 1 hour per verb

34. 10/9/01PropBank34 Automatic Predicate Argument Tagger  Predicate argument labels Uses TreeBank “cues” Consults lexical semantic KB —Hierarchically organized verb subcategorization frames and alternations associated with tree templates —Ontology of noun-phrase referents —Multi-word lexical items Matches annotated tree templates against parse in Tree- adjoining Grammar style standoff annotation in external file referencing treenodes  Preliminary accuracy rate of 83.7% (800+ predicates)

35. 10/9/01PropBank35 Summary  Predicate-argument structure labels are arbitrary to a certain degree, but still consistent, and generic enough to be mappable to particular theoretical frameworks  Automatic tagging as a first pass makes the task feasible  Agreement and accuracy figures are reassuring

36. 10/9/01PropBank36 Solomonization Source tree: Intel told analysts that the company will resume shipments of the chips within two to three weeks. *** kate said: arg0 : Intel arg1 : the company will resume shipments of the chips within two to three weeks arg2 : analysts *** erwin said: arg0 : Intel arg1 : that the company will resume shipments of the chips within two to three weeks arg2 : analysts

37. 10/9/01PropBank37 Solomonization Such loans to Argentina also remain classified as non-accruing, *TRACE*-1 costing the bank $ 10 million *TRACE*-*U* of interest income in the third period. *** kate said: argM-TMP : in the third period arg3 : the bank arg2 : $ 10 million *TRACE*-*U* of interest income arg1 : *TRACE*-1 *** erwin said: argM-TMP : in the third period arg3 : the bank arg2 : $ 10 million *TRACE*-*U* of interest income arg1 : *TRACE*-1 Such loans to Argentina

38. 10/9/01PropBank38 Solomonization Also, substantially lower Dutch corporate tax rates helped the company keep its tax outlay flat relative to earnings growth. *** kate said: argM-MNR : relative to earnings growth arg3-PRD : flat arg1 : its tax outlay arg0 : the company *** katherine said: argM-ADV : relative to earnings growth arg3-PRD : flat arg1 : its tax outlay arg0 : the company