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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

INTRODUCTION TO ARTIFICIAL INTELLIGENCE. Massimo Poesio Relation Extraction . Powell met Zhu Rongji. battle. wrestle. join. debate. Powell and Zhu Rongji met. consult. Powell met with Zhu Rongji. Proposition: meet (Powell , Zhu Rongji ). Powell and Zhu Rongji had a meeting.

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

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  1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE Massimo PoesioRelation Extraction

  2. Powell met Zhu Rongji battle wrestle join debate Powell and Zhu Rongji met consult Powell met with Zhu Rongji Proposition:meet(Powell, Zhu Rongji) Powell and Zhu Rongji had a meeting SEMANTIC INTERPRETATION: FROM SENTENCES TO PROPOSITIONS meet(Somebody1, Somebody2) . . . When Powell met Zhu Rongji on Thursday they discussed the return of the spy plane. meet(Powell, Zhu) discuss([Powell, Zhu], return(X, plane))

  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)) Filmore, 68 Jackendoff, 72 agent(A) -> intentional(A), sentient(A), causer(A), affector(A) patient(P) -> affected(P), change(P),… 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. a GM-Jaguar pact give(GM-J pact, US car maker, 30% stake) PROPBANK REPRESENTATION a GM-Jaguar pact that would give the U.S. car maker an eventual 30% stake in the British company. Arg0 that would give Arg1 *T*-1 an eventual 30% stake in the British company Arg2 the US car maker

  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 • the frames (e.g. Compliance) that they evoke, • the frame elements (semantic roles) that make up the components of the frames (in Compliance, Norm is one such frame element), and • 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. CLASSIFICATION SCHEMES FOR NOMINAL RELATIONS

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

  20. THE TWO-LEVEL TAXONOMY OF RELATIONS, 2

  21. 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

  22. 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

  23. TEMPLATE-FILLING

  24. EXAMPLE MUC: JOB POSTING

  25. THE ASSOCIATED TEMPLATE

  26. AUTOMATIC CONTENT EXTRACTION (ACE)

  27. ACE: THE DATA

  28. ACE: THE TASKS

  29. RELATION DETECTION AND RECOGNITION

  30. ACE: RELATION TYPES

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

  32. THE TASK OF SEMANTIC RELATION EXTRACTION

  33. SEMANTIC RELATION EXTRACTION: THE CHALLENGES

  34. 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

  35. MORE COMPLEX SEMANTICS • Modalities • Temporal interpretation

  36. ACKNOWLEDGMENTS • Many slides borrowed from • Roxana Girju • Alberto Lavelli

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