1. The Encoding of Lexical Implications in VerbNet Change of Location Predicates Annie Zaenen, Danny Bobrow and Cleo Condoravdi

2. Outline Rationale of the talk and aims of the project VerbNet information Conclusions

3. Reusability Lexical resources are expensive to create The best way to create them is collaboratively and to structure them in such a way that they can be used for several different projects In how far is VerbNet information reusable?

4. Inferences about locations: context Application: Question Answering/textual inference Method: create a Knowledge Representation for a text, e.g. a newspaper article and a question/inference subsumption calculation to see whether the information in the question is contained in the text Approach: normalize the text to a logical form through rewrite rules.

5. Example 1

6. Aim of subproject Use VN information to make inferences about change of location of event participants E.g. Annie went from San Francisco to Morocco ==> Annie was in San Francisco at t1 & Annie was in Morocco at t2 & t1 < t2 In our representation:

7. Representation before change of location calculation Annie left San Francisco for Morocco. Conceptual Structure: subconcept(leave:7,[leave-1,…]) role(Theme,leave:7,Annie:1) role(Source,leave:7,San Francisco:12) role(Destination,leave:7,Morocco:30) subconcept(Annie:1,[female-2]) subconcept(Morocco:30,[location-1,location-4]) subconcept(San Francisco:12,[location-1,location-4]) Temporal Structure: temporalRel(startsAfterEndingOf,Now,leave:7) [within square brackets: information from WN]

8. Target Representation 2 Annie left San Francisco for Morocco. Conceptual Structure subconcept(locate48,[locate-1,locate-3]) subconcept(locate47,[locate-1,locate-3]) role(Theme,locate48,Annie:1) role(Theme,locate47,Annie:1) role(Location,locate48,Morocco:30) role(Location,locate47,San Francisco:12) subconcept(leave:7,[leave-1…]) role(Source,leave:7,San Francisco:12) role(Theme,leave:7,Annie:1) role(Destination,leave:7,Morocco:30) subconcept(Annie:1,[female-2]) subconcept(Morocco:30,[location-1,location-4]) subconcept(San Francisco:12,[location-1,location-4]) Temporal Structure: temporalRel(startsAfterEndingOf,Now,leave:7) temporalRel(startsAfterEndingOf,locate48,leave:7) temporalRel(startsAfterEndingOf,leave:7,locate47)

9. Getting the necessary information How do we know that the moving object in a sentence like ‘John left New York’ is John? Where do we get information that tells us that before the event described in the sentence ‘John is in New York’ and that afterwards he is no longer there?

10. Outline Rationale and aims of the project. VerbNet information –VerbNet Semantics –Change of Location predicates in VerbNet Conclusions

11. Can we use VerbNet information for this? Levin classes VN2-1: 239 XML-files representing Levin (sub)classes + some additions

12. VerbNet ‣ Verb classes in VN are based on Levin classification. This classification embodies the belief that there is a close correspondence between (some aspects of) the meaning of verbs and their subcategorization alternations (John sank the boat; the boat sank). ‣ Verbs are classified based on their subcategorization alternations. ‣ VerbNet adds the thematic role information and a semantic representation.

13. VN information Class: Send-11.1 Thematic roles: –Agent, Theme, Source, Destination Selectional restrictions: –Agent[+animate] or+[organization], –Theme[+concrete], –Source[+location], –Destination[+location] Frames: –Name: NP-PP-PP –Example: Nora sent the book from London to Paris. –Syntax: Agent V Theme Source Destination –Semantics »cause(agent,E) »motion(during(E),Theme) »location(start(E),Theme, Source) »location(end(E),Theme,Destination)

14. Why not use VN semantic roles? “ Theme: used for participants in a location or undergoing a change of location.” “Agent: generally a human or animate subject. Used mostly as a volitional agent, but also used in VerbNet for internally controlled subjects such as forces and machines.” “Destination: end point of the motion, or direction towards which the motion is directed. … “Source: start point of the motion. Usually introduced by a source prepositional phrase (mostly headed by ‘from or ‘out of’.) “Location: underspecified destination, source or place in general introduced by a locative or path prepositional phrase.” from Kipper (2005)

15. Use VN semantics Event structure in VN: based on Moens and Steedman (1988) Not all events have culminations; if there is no culmination there is no result state. –John built a house : culmination (accomplishment) –John played the piano : no culmination (activity) StartResultEndDuring Culmination

16. VN semantics (event structure) Send Agent, Theme, Destination Amanda sent the package to New York. motion(during(E), Theme), location(end(E), Theme, Destination), cause(Agent, E0)

17. VN semantics (event structure) Shove motion(during(E), Theme), location(end(E), Theme, Destination), cause(Agent, E)

18. A problem that we will ignore No free variation: to/towards Prepositions (Kipper Schuler, 2005) Spatial –path »src: from, out, out of, … »dir: across, along, around, down, … »dest: dest-conf: into, onto, … dest-dir: for, at, to, towards, … –loc: about, above, against, … What is … Further distinction necessary: –They sent the kids into the mountains. –They slid the books onto the table.

19. Combinations Slide-11.2: The books slid from the desk to the floor. motion(during(E), Theme), location(start(E), Theme, Source), location(end(E), Theme, Destination) Carry-11.4: Nora carried the books to Paris. equal(E0,E1) motion(during(E0), Theme), location(end(E0), Theme, Destination), motion(during(E1), Agent) location(end(E0), Agent, Destination), cause(Agent, E) Send-11.1: Nora sent the books to London. motion(during(E), Theme), location(end(E), Theme, Destination), cause(Agent, E)

20. Another promising pattern Class-9.1: put –motion(during(E), Theme), –not(Prep(start(E), Theme, Destination)), –Prep(end(E), Theme, Destination), –cause(Agent, E) Class-9.3.2: The water rushed into the house. –motion(during(E), Theme), –not(Prep(start(E), Theme, Destination)), –Prep(end(E), Theme, Destination) Here the idea is that the value of the preposition has to be factored in. How the VN people saw this exactly is not of our concern. Given the right semantics for the preposition we can use the information.

21. Small classes class 16-concealment –(She hid the presents in the drawer.), ‘location(result(E),Patient, Location)’ class-22 (mix, shake, tape, etc.) –‘mingled(result(E),physical,Patient1,Patient2) together(end(E),physical,Patient1,Patient2)’ class-23 –‘together(start(E),physical,Patient1,Patient2), apart(end(E),physical,Patient1,Patient2)’ class-47.5.2 –‘not(together(start(E),physical,Theme i,Theme j )), together(end(E),physical,Theme i,Theme j )’ The representation is not analytic enough to get an invariant representation of change of location in these cases

22. Incomplete coverage in classes that are covered in principle class 9.3 (funnel), only endpoints are given –Funnel the liquid from the bottle into the cup. class 9.5 (pour), no frame with both start and end points –He poured the water from the bowl into the cup class 9.7 –OK: Jessica loaded boxes into the wagon –Not: Jessica loaded the boxes from the train into the car. 10.2 (banish) and class 10.4.2 (shovel) both a source and a destination frame are given but no frame that combines the two. –Shovel the snow from the sidewalk into the ditch.

23. Potential VN classes with start and end locations Classes that seemed good candidates to me: 9.1- 3(put,funnel), 9.4(drop), 9.5-10(spray,butter etc.), 10(removal), 11(send), 12(push), 16(concealment), 17(throw), 18.1,2,4(impact), 22(attach/combine), 23(disassemble), 43.2,3(roar/flutter), 47.5,7(meander), 47.8(contiguous location), 48(appear/disappearance), 50(assuming position), 51.1-7(motion), 53.2(rushing), 59(force), 80(withdraw), 89(settle), 99(commit) Classes that have one of the two patterns described earlier: 9.1-3, 9.5-10, 10, 11, 17, 48, 51.2, 51.7, 51.8, 99.

24. Incomplete coverage Class-51(verbs of motion) : run, dance, skate, etc. (motion(during(E),Theme)) (Prep(E,Theme,Location)) –Mary ran in the forest. –Mary ran into the forest. –Mary carried the package in her pocket. Class-47: meander etc: only stative meaning –The path meandered through the valley. –The troops meandered through the valley.

25. What is an argument, what is a sense, what is a frame? Palmer et al. 1999, and Dang et al. 2000 The bottle floated into the cave. The train roared into the station. The bottle floated. The train roared. ==> float and roar don’t have inherent paths; the path information has to be adjoined. Levin Rappoport Hovav 1995: Roar is polysemous VN will only have paths when the constructors of VN deemed the path to be inherent.

26. A compositional approach Float exist(during(E),Theme),Prep(during(E),Theme,Location), motion(during(E),Theme) Float does not require a path but is compatible with one. Other verbs require paths, and still others are incompatible with them This can be done with a simple feature in TAG and other frameworks. But it is not intuitively clear which verbs the authors of VN consider to be in each class, so the user has to go through all the (sub)classes to find out which bit of information is given or not.

27. Outline Rationale and aims of the project. VerbNet information Conclusions

28. Conclusions about VerbNet The subcategorization frames that VN handles are very incomplete (depends on what was in Levin’s book.) VN Semantic structure is a promising piece of information to key lexical entailments off but, given it is not clear what will be spelled out for each class, the user has to go through all the classes and subclasses. At that point one has done as much work as would be required for associating the semantic information from scratch with each class or subclass. Reusability of semantic information doesn’t seem optimal.

29. What can be done? In this particular case: –systematic study of how prepositional phrase information and verb information combines in change of location expressions. –Better understanding of what information should be contained in subcategorization frames (has translating for FrameNet to VerbNet and back and then to something else again taught us something? More generally: given the means available for resource development, most likely not very much but –better documentation would be of some help. –more joint development??? –or is usability what we should aim for and should we just forget about reusability?

30. Some of our rules ![+instantiable(%VerbSk,t), vn_semantics(%VerbSk,location(start(E),%Theme,%Source)), +role(%Theme, %VerbSk, %Arg1), +role(%Source, %VerbSk, %Arg2), {new_constant(locate,%LocSk)} ]! ==> new_locate(%LocSk, %Arg1, %Arg2, %VerbSk, pos, before). ![+instantiable(%VerbSk,t), vn_semantics(%VerbSk,not(location(start(E),%Theme,%Source))), +role(%Theme, %VerbSk, %Arg1), +role(%Source, %VerbSk, %Arg2), {new_constant(locate,%LocSk)} ]! ==> new_locate(%LocSk, %Arg1, %Arg2, %VerbSk, neg, before). ![+instantiable(%VerbSk,t), vn_semantics(%VerbSk,location(end(E),%ThemeRole,%ToLocRole)), +role(%ThemeRole, %VerbSk, %Mover), +role(%ToLocRole, %VerbSk, %ToLoc), {new_constant(locate,%LocSk)} ]! ==> new_locate(%LocSk, %Mover, %ToLoc, %VerbSk, pos, after).

31. Some of our rules "verb skolem in negative context” ![+uninstantiable(%VerbSk,t), vn_semantics(%VerbSk,location(end(E),%ThemeRole,%ToLocRole)), +role(%ThemeRole, %VerbSk, %Mover), +role(%ToLocRole, %VerbSk, %ToLoc), {new_skolem(locate,%LocSk)} ]! ==> new_locate(%LocSk, %Mover, %ToLoc, %VerbSk, neg, after). ![+uninstantiable(%VerbSk,t), vn_semantics(%VerbSk,not(location(end(E),%ThemeRole,%ToLocRole))), +role(%ThemeRole, %VerbSk, %Mover), +role(%ToLocRole, %VerbSk, %ToLoc), {new_skolem(locate,%LocSk)} ]! ==> new_locate(%LocSk, %Mover, %ToLoc, %VerbSk, pos, after).

32. Some of our rules +new_locate(%LocSk, %Mover, %Loc, %VerbSk, pos, %), cached_hypers(locate, %Hypers) ==> subconcept(%LocSk, %Hypers), instantiable(%LocSk, t), role(Location, %LocSk, %Loc), role(Theme, %LocSk, %Mover). +new_locate(%LocSk, %Mover, %Loc, %VerbSk, neg, %), cached_hypers(locate, %Hypers) ==> subconcept(%LocSk, %Hypers), uninstantiable(%LocSk, t), role(Location, %LocSk, %Loc), role(Theme, %LocSk, %Mover). +new_locate(%LocSk, %Mover, %Loc, %VerbSk, %, before) ==> temporalRel(startsAfterEndingOf, %VerbSk, %LocSk). +new_locate(%LocSk, %Mover, %Loc, %VerbSk, %, after) ==> temporalRel(startsAfterEndingOf, %LocSk, %VerbSk).

33. Thanks

34. Annotation At this point annotation of corpora and lexical resources is necessary for ‘deep’ language understanding Formal correspondences can be derived from aligned corpora Meaning correspondences would need an alignment between the text and the world In some cases this correspondence can be approximated with a text-to-text correspondences but not in all, e.g. mapping to knowledge representation

35. Annotation ✓ Corpus annotation: running text is annotated, e.g. coreference ✓ Annotation of lexical resources, e.g. verb classes

36. Annotation Annotation related to meaning is difficult ‣ Often the distinctions that need to be made are not well understood (e.g. animacy, coreference) ‣ Often the relation to the applications isn’t clear (e.g. thematic roles)

37. Thematic Roles Thematic role labels are used in linguistic theory to represent (some of the) inferences that the use of specific verbs (or adjectives or deverbal nouns) licenses. Example : John sank the boat (theme) The boat (theme) sank The label indicates that (some of the inferences) are the same in both cases, namely in both sentences ‘the boat’ changes location.

38. VN and inference  Thematic roles are entailments.  Based on the thematic role information in VN we should be able to derive (some of the) entailments of the verb.

39. Thematic roles as entailments  Thematic roles are meant to mediate between syntactic subcategorization and lexical semantics. They need to be charactizable in semantic terms, otherwise their syntactic use is facuous.  In principle then, thematic roles can be cached in as a set of entailments. For instance, because the boat is the theme in ‘John sank the boat’, we know the answer to ‘What sank’ as well as that to ‘What did John sink?’ and ‘What was sunk?’

40. Thematic roles as entailments General mapping theory Under a general approach to lexical mapping, the same thematic role label would be used for the same inference across all verbs. For instance, in English, verbs such as kiss and hit have both a SUBJECT and an OBJECT. In both cases the referent of the SUBJECT does the action and the referent of the OBJECT undergoes the action. This means that both verbs have the same thematic role for their SUBJECT and for their OBJECT, agent and patient respectively. (It is, of course, not assumed that the mapping is one-to-one: for instance, please has an experiencer object)

41. Thematic roles as entailments Narrow mapping theory Under a narrow conception of thematic role labeling, different verb classes have different role labels and generalizations are only possible within a verb class (cf. FrameNet)We then have roles like buyer, seller, …

42. What kind of approach is used in Verbnet? The documentation for VerbNet is incomplete and shattered (Karin Kipper’s thesis, some articles, a couple of files on the web site, …)

43. The characterization of thematic roles in Kipper (2005) “ Theme: used for participants in a location or undergoing a change of location.” “Agent: generally a human or animate subject. Used mostly as a volitional agent, but also used in VerbNet for internally controlled subjects such as forces and machines.” “Destination: end point of the motion, or direction towards which the motion is directed. … “Source: start point of the motion. Usually introduced by a source prepositional phrase (mostly headed by ‘from or ‘out of’.) “Location: underspecified destination, source or place in general introduced by a locative or path prepositional phrase.”

44. Not everything that moves is a theme John sent the package to New York. Theme: package; Destination: New York. John carried the package to New York. Theme: the package; Destination: New York. and what about John and Mary in the following? John followed Mary to New York. VN says: agent for John and theme for Mary John followed Mary with a telescope. ?? John followed Mary to the gate with a telescope.

45. Not all themes move He lives in Hong Kong. Theme with verbs of existence (reside, live, loom, …) The tourists admired the paintings. Experiencer ; Theme The children liked that the clown had a red nose. Experiencer; Theme

46. What is a source, what is a path? The horse jumped over the fence. Theme; Location [+spatial] Out of the box jumped a little white rabbit. Location [+path]; Theme The convict escaped from the prison. Theme; Location [+path] No destination or source argument for these verbs, but: The books slid from the table. Theme; Source

47. Patients and Themes Hit: throw-17.1-1 Steve hit the ball from the corner to the garden. Agent, Theme[concrete(+)]), Source, Destination Hit: hit-18.1-1 Paula hit the ball with a stick. Agent, Patient[concrete(+)]), Instrument Paula hit the ball from the corner to the center of the field with a stick.

48. VN semantics (event structure) Shove Agent, Theme, Destination Amanda shoved the package to the corner. motion(during(E),Theme), location(end(E), Theme, Destination), cause(Agent, E)

49. VN semantics (event structure) LiveTheme, Location exist(during(E), Theme), Prep(E,Theme, Location). AdmireExperiencer, Theme emotional_state(E, Emotion, Experiencer), in_reaction_to(E, Theme).

50. VN semantics (event structure) deport The king deported the general to the isle. cause(Agent, E), location(end(E), Theme, Destination) banish The king banished the general to the isle. cause(Agent, E), location(end(E), Theme, Destination)

51. A simple semantic pattern that seems to work send class: Nora sent the book from Paris to London motion(during(E), Theme), location(start(E), Theme, Source), location(end(E), Theme, Destination), cause(Agent, E) NP(Agent,[]),verb, NP(Theme,[]), Prep(any,[`=(src,+)]), NP(Source,[]), Prep(to,[]),NP(Destination,[])

52. Conclusions about VerbNet VN thematic roles need to be combined with verb class information but at that point the verb class and the subcategorization information can do the job without the the thematic role. Event structure is more useful if one is after probable entailments Both only encode a subset of the entailments one might be interested in.