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Lexical Semantics at Penn: Proposition Bank and VerbNet

Lexical Semantics at Penn: Proposition Bank and VerbNet. Martha Palmer, Dan Gildea, Paul Kingsbury, Olga Babko-Malaya, Bert Xue, Karin Kipper, Hoa Dang, Szuting Yi, Edward Loper, Jinying Chen August 22, 2003. Outline. Verbs and their semantic roles The part played by word senses

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Lexical Semantics at Penn: Proposition Bank and VerbNet

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  1. Lexical Semantics at Penn: Proposition Bank and VerbNet Martha Palmer, Dan Gildea, Paul Kingsbury, Olga Babko-Malaya, Bert Xue, Karin Kipper, Hoa Dang, Szuting Yi, Edward Loper, Jinying Chen August 22, 2003

  2. Outline • Verbs and their semantic roles • The part played by word senses • Mapping Propbank sense distinctions to other sense inventories • VerbNet entries for individual, sense tagged verbs

  3. Predicate-Argument Structure They signed the document in spite of his objections. sign Agent: They Theme: the document NP1[case:nom]NP2[case:acc] ArgM: in spite of his objections Arg0: They REL: signed Arg1: the document ArgM-ADV: in spite of his objections.

  4. Capturing semantic roles* • Charles 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/

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

  6. Arg0 that would give Arg1 *T*-1 an eventual 30% stake in the British company Arg2 a GM-Jaguar pact the US car maker The same sentence, PropBanked (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)))))))))))) have been expecting Arg1 Arg0 Analysts expect(Analysts, GM-J pact) give(GM-J pact, US car maker, 30% stake)

  7. 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?

  8. Fine-grained WordNet Senses • Senseval 2 – WSD Bakeoff, using WordNet 1.7 (avg polysemy: 16, ITA: 71%, best system: 59.6%) Verb Develop WN1: CREATE, MAKE SOMETHING NEW They developed a new technique WN2: CREATE BY MENTAL ACT They developed a new theory of evolution develop a better way to introduce crystallography techniques? WN1? WN2?

  9. WN Senses: verb ‘develop’ WN1 WN2 WN3 WN4 WN6 WN7 WN8 WN5 WN 9 WN10 WN11 WN12 WN13 WN 14 WN19 WN20

  10. Sense Groups: verb ‘develop’ (Avg polysemy: 8, ITA: 82%, best system: 69%) WN1 WN2 WN3 WN4 WN6 WN7 WN8 WN5 WN 9 WN10 WN11 WN12 WN13 WN 14 WN19 WN20

  11. PropBank Framesets for ‘develop’ • Develop.02 (sense: create/improve) Arg0: agent Arg1: thing developed Example: They developed a new technique. • Develop.01 (sense: come about) Arg1: non-intentional theme Example: The child developed beautifully.

  12. 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

  13. Sense Hierarchy • Framesets – coarse grained distinctions • Sense Groups (Senseval-2) intermediate level (includes Levin classes) – 95% overlap • WordNet – fine grained distinctions

  14. Limitations to WordNet • Poor inter-annotator agreement • Just sense tags - no representations • Very little mapping to syntax • No predicate argument structure • no selectional restrictions • No generalizations about sense distinctions

  15. VerbNet • Computational verb lexicon • Clear association between syntax and semantics • Syntactic frames (LTAGs) and selectional restrictions (WordNet) • Lexical semantic information – predicate argument structure • Semantic components represented as predicates • Links to WordNet senses • Entries based on refinement of Levin Classes • Inherent temporal properties represented explicitly • during(E), end(E), result(E)

  16. VerbNet Class entries: • Verb classes allow us to capture generalizations about verb behavior • Verb classes are hierarchically organized • Members have common semantic elements, thematic roles, syntactic frames and coherent aspect Verb entries: • Each verb can refer to more than one class (for different senses) • Each verb sense has a link to the appropriate synsets in WordNet (but not all senses of WordNet may be covered) • A verb may add more semantic information to the basic semantics of its class

  17. Develop.02 “ create” – VerbNet • Levin class: grow-26.2 , WordNet Senses: WN 10, 12, 13, 14 • Thematic Roles: Agent[+animate] Material[+concrete] Product[+concrete] • Semantics: ¬exist(start(E),Product), exist(result(E),Product), made_of(result(E),Product,Material), cause(Agent,E) • Frames Causative/Inchoative Alternation (causative, Material Object) The gardener developed that acorn into an oak tree Causative/Inchoative Alternation (causative, Product Object) The gardener developed an oak tree from that acorn Material/Product Alternation Intransitive (Material Subject) That acorn will develop into an oak tree Material/Product Alternation Intransitive (Product Subject) An oak tree will develop from that acorn

  18. Develop.01 “come about” – VerbNet • Levin Class: appear-48.1.1, WordNet Senses: WN5 • Thematic Roles : Location, Theme • Semantics: at(end(E),Theme, Location) • Frames Basic Intransitive () Intransitive (+ Location PP) Locative Inversion (Most verbs) There-insertion (Most verbs)

  19. Lexical Semantics at Penn • Annotation of Penn Treebank with semantic role labels (propositions) and sense tags • Links to VerbNet and WordNet • Provides additional semantic information that clearly distinguishes verb senses • Class based to facilitate extension to previously unseen usages

  20. Backup slides

  21. Annotation procedure • Extraction of all sentences with given verb • First pass: Automatic tagging (Joseph Rosenzweig) • http://www.cis.upenn.edu/~josephr/TIDES/index.html#lexicon • Second pass: Double blind hand annotation • ITA high 80’s to low 90’s • Third pass: adjudication • Tagging tool highlights inconsistencies

  22. Levin classes (3100 verbs) • 47 top level classes, 193 second and third level • Based on pairs of syntactic frames. • John broke the jar. / Jars break easily. / The jar broke. • John cut the bread. / Bread cuts easily. / *The bread cut. • John hit the wall. / *Walls hit easily. / *The wall hit. • Reflect underlying semantic components • contact, directed motion, • exertion of force, change of state • Synonyms, syntactic patterns (conative), relations

  23. Hit class – hit-18.1 MEMBERS:[bang(1,3),bash(1),... hit(2,4,7,10), kick (3),...] THEMATIC ROLES: Agent, Patient, Instrument SELECT RESTRICTIONS: Agent(int_control), Patient(concrete), Instrument(concrete) FRAMES and PREDICATES:

  24. VerbNet/WordNet

  25. Action Hierarchy for Maintenance Domain

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