1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE Massimo Poesio Relation Extraction

2. SEMANTIC INTERPRETATION: 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)

3. OTHER ASPECTS OF SEMANTIC INTERPRETATION Identification of RELATIONS between entities mentioned – Focus of interest in modern CL since 1993 or so Identification of TEMPORAL RELATIONS – From about 2003 on QUALIFICATION of such relations (modality, epistemicity) – From about 2010 on

4. TYPES OF RELATIONS Predicate-argument structure (verbs and nouns) Nominal relations Relations between events / temporal relations

5. PREDICATE-ARGUMENT STRUCTURE Linguistic Theories – Case Frames – Fillmore  FrameNet – Lexical Conceptual Structure – Jackendoff  LCS – Proto-Roles – Dowty  PropBank – English verb classes (diathesis alternations) - Levin  VerbNet – Talmy, Levin and Rappaport

6. Fillmore’s Case Theory Sentences have a DEEP STRUCTURE with CASE RELATIONS A sentence is a verb + one or more NPs – Each NP has a deep-structure case A(gentive) I(nstrumental) D(ative) F(actitive) L(ocative) O(bjective) – Subject is no more important than Object Subject/Object are surface structure

7. THEMATIC ROLES Following on Fillmore’s original work, many theories of predicate argument structure / thematic roles were proposed, among which the best known perhaps – Jackendoff’s LEXICAL CONCEPTUAL SEMANTICS – Dowty’s PROTO-ROLES theory

8. Dowty’s PROTO-ROLES Event-dependent Prototypes based on shared entailments Grammatical relations such as subject related to observed (empirical) classification of participants Typology of grammatical relations Proto-Agent Proto-Patient

9. Proto-Agent Properties – Volitional involvement in event or state – Sentience (and/or perception) – Causing an event or change of state in another participant – Movement (relative to position of another participant) – (exists independently of event named) *may be discourse pragmatic

10. Proto-Patient Properties: – Undergoes change of state – Incremental theme – Causally affected by another participant – Stationary relative to movement of another participant – (does not exist independently of the event, or at all) *may be discourse pragmatic

11. Semantic role labels: Jan broke the LCD projector. break (agent(Jan), patient(LCD-projector)) cause(agent(Jan), change-of-state(LCD-projector)) (broken(LCD-projector)) agent(A) -> intentional(A), sentient(A), causer(A), affector(A) patient(P) -> affected(P), change(P),… Filmore, 68 Jackendoff, 72 Dowty, 91

12. VERBNET AND PROPBANK Dowty’s theory of proto-roles was the basis for the development of PROPBANK, the first corpus annotated with information about predicate-argument structure

13. PROPBANK REPRESENTATION a GM-Jaguar pact that would give *T*-1 the US car maker an eventual 30% stake in the British company Arg0 Arg2 Arg1 give(GM-J pact, US car maker, 30% stake) a GM-Jaguar pact that would give the U.S. car maker an eventual 30% stake in the British company.

14. ARGUMENTS IN PROPBANK Arg0 = agent Arg1 = direct object / theme / patient Arg2 = indirect object / benefactive / instrument / attribute / end state Arg3 = start point / benefactive / instrument / attribute Arg4 = end point Per word vs frame level – more general?

15. FROM PREDICATES TO FRAMES In one of its senses, the verb observe evokes a frame called Compliance: this frame concerns people’s responses to norms, rules or practices. The following sentences illustrate the use of the verb in the intended sense: – Our family observes the Jewish dietary laws. – You have to observe the rules or you’ll be penalized. – How do you observe Easter? – Please observe the illuminated signs.

16. FrameNet FrameNet records information about English words in the general vocabulary in terms of 1.the frames (e.g. Compliance) that they evoke, 2.the frame elements (semantic roles) that make up the components of the frames (in Compliance, Norm is one such frame element), and 3.each word’s valence possibilities, the ways in which information about the frames is provided in the linguistic structures connected to them (with observe, Norm is typically the direct object). theta

17. NOMINAL RELATIONS

18. HISTORY

19. CLASSIFICATION SCHEMES FOR NOMINAL RELATIONS

20. ONE EXAMPLE (Barker et al1998, NASTASE & Spakowicz 2003)

21. THE TWO-LEVEL TAXONOMY OF RELATIONS, 2

22. THE SEMEVAL-2007 CLASSIFICATION OF RELATIONS Cause-Effect: laugh wrinkles Instrument-Agency: laser printer Product-Producer: honey bee Origin-Entity: message from outer-space Theme-Tool: news conference Part-Whole: car door Content-Container: the air in the jar

23. CAUSAL RELATIONS

24. TEMPORAL RELATIONS

25. THE MUC AND ACE TASKS Modern research in relation extraction, as well, was kicked-off by the Message Understanding Conference (MUC) campaigns and continued through the Automatic Content Extraction (ACE) and Machine Reading follow- ups MUC: NE, coreference, TEMPLATE FILLING ACE: NE, coreference, relations

26. TEMPLATE-FILLING

27. EXAMPLE MUC: JOB POSTING

28. THE ASSOCIATED TEMPLATE

29. AUTOMATIC CONTENT EXTRACTION (ACE)

30. ACE: THE DATA

31. ACE: THE TASKS

32. RELATION DETECTION AND RECOGNITION

33. ACE: RELATION TYPES

34. OTHER PRACTICAL VERSIONS OF RELATION EXTRACTION Biomedical domain (BIONLP, BioCreative) Chemistry Cultural Heritage

35. THE TASK OF SEMANTIC RELATION EXTRACTION

36. SEMANTIC RELATION EXTRACTION: THE CHALLENGES

37. HISTORY OF RELATION EXTRACTION Before 1993: Symbolic methods (using knowledge bases) Since then: statistical / heuristic based methods – From 1995 to around 2005: mostly SUPERVISED – More recently: also quite a lot of UNSUPERVISED / SEMI SUPERVISED techniques

38. MORE COMPLEX SEMANTICS Modalities Temporal interpretation

39. ACKNOWLEDGMENTS Many slides borrowed from – Roxana Girju – Alberto Lavelli