INTRODUCTION TO ARTIFICIAL INTELLIGENCE Massimo Poesio Relation Extraction

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)

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

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

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

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

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

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

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

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

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

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

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.

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?

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.

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

NOMINAL RELATIONS

HISTORY

CLASSIFICATION SCHEMES FOR NOMINAL RELATIONS

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

THE TWO-LEVEL TAXONOMY OF RELATIONS, 2

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

CAUSAL RELATIONS

TEMPORAL RELATIONS

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

TEMPLATE-FILLING

EXAMPLE MUC: JOB POSTING

THE ASSOCIATED TEMPLATE

AUTOMATIC CONTENT EXTRACTION (ACE)

ACE: THE DATA

ACE: THE TASKS

RELATION DETECTION AND RECOGNITION

ACE: RELATION TYPES

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

THE TASK OF SEMANTIC RELATION EXTRACTION

SEMANTIC RELATION EXTRACTION: THE CHALLENGES

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

MORE COMPLEX SEMANTICS Modalities Temporal interpretation

ACKNOWLEDGMENTS Many slides borrowed from – Roxana Girju – Alberto Lavelli