1. ARDA Visit 1 Penn 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. ARDA Visit 2 Penn 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. ARDA Visit 3 Penn 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. ARDA Visit 4 Penn 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 *See also Framenet, http://www.icsi.berkeley.edu/~framenet/http://www.icsi.berkeley.edu/~framenet/

5. ARDA Visit 5 Penn A TreeBanked Sentence Analyst s S NP- SBJ VP have VP beenVP 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. ARDA Visit 6 Penn The same sentence, PropBanked Analyst s 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. ARDA Visit 7 Penn 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. ARDA Visit 8 Penn 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. ARDA Visit 9 Penn WN Senses: verb ‘develop’ WN1 WN2 WN3 WN4 WN6 WN7 WN8 WN5 WN 9 WN10 WN11 WN12 WN13 WN 14 WN19 WN20

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

11. ARDA Visit 11 Penn 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. ARDA Visit 12 Penn Overlap between Groups and Framesets – 95% WN1 WN2 WN3 WN4 WN6 WN7 WN8 WN5 WN 9 WN10 WN11 WN12 WN13 WN 14 WN19 WN20 Frameset1 Frameset2

13. ARDA Visit 13 Penn Sense Hierarchy  Framesets – coarse grained distinctions  Sense Groups (Senseval-2) intermediate level (includes Levin classes) – 95% overlap  WordNet – fine grained distinctions

14. ARDA Visit 14 Penn 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. ARDA Visit 15 Penn 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. ARDA Visit 16 Penn 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. ARDA Visit 17 Penn 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. ARDA Visit 18 Penn 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. ARDA Visit 19 Penn 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. ARDA Visit 20 Penn  Backup slides

21. ARDA Visit 21 Penn 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. ARDA Visit 22 Penn 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. Basic TransitiveA V P cause(Agent,E) /\ manner (during(E),directedmotion,Agent)/\ manner (end(E), forceful,Agent)/\ contact(end(E),Agent,Patient) ConativeAV at Pmanner (during (E), directedmotion, Agent) ¬contact(end(E),Agent,Patient) With/against alternationA V I against/on P cause(Agent, E) /\ manner(during (E),directedmotion, Instr)/\ manner(end(E), forceful, Instr)/\ contact (end(E), Instr, Patient) 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: Hit class – hit-18.1

24. ARDA Visit 24 Penn VerbNet/WordNet

25. ARDA Visit 25 Penn Action Hierarchy for Maintenance Domain