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INTRODUCTION TO ARTIFICIAL INTELLIGENCE. Massimo Poesio Supervised Relation Extraction. RE AS A CLASSIFICATION TASK. Binary relations Entities already manually/automatically recognized Examples are generated for all sentences with at least 2 entities

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Introduction to artificial intelligence

INTRODUCTION TO ARTIFICIAL INTELLIGENCE

Massimo PoesioSupervised Relation Extraction


Re as a classification task
RE AS A CLASSIFICATION TASK

  • Binary relations

  • Entities already manually/automatically recognized

  • Examples are generated for all sentences with at least 2 entities

  • Number of examples generated per sentence isNC2 – Combination of N distinct entities selected 2 at a time





The supervised approach to re
THE SUPERVISED APPROACH TO RE

  • Most current approaches to RE are kernel-based

  • Different information is used

    • Sequences of words, e.g., through the GLOBAL CONTEXT / LOCAL CONTEXT kernels of Bunescu and Mooney / GiulianoLavelli & Romano

    • Syntactic information through the TREE KERNELS of Zelenko et al / Moschitti et al

    • Semantic information in recent work


Kernel methods a reminder
KERNEL METHODS: A REMINDER

  • Embedding the input data in a feature space

  • Using a linear algorithm for discovering non-linear patterns

  • Coordinates of images are not needed, only pairwise inner products

  • Pairwiseinner products can be efficiently computed directly from X using a kernel function K:X×X→R



The word sequence approach
THE WORD-SEQUENCE APPROACH

  • Shallow linguistic Information:

    • tokenization

    • Lemmatization

    • sentence splitting

    • PoStagging

Claudio Giuliano, Alberto Lavelli, and Lorenza Romano (2007), FBK-IRST: Kernel methods for relation extraction, Proc. Of SEMEVAL-2007


Linguistic realization of relations
LINGUISTIC REALIZATION OF RELATIONS

Bunescu & Mooney, NIPS 2005


Word sequence kernels
WORD-SEQUENCE KERNELS

  • Two families of “basic” kernels

    • Global Context

    • Local Context

  • Linear combination of kernels

  • Explicit computation

    • Extremely sparse input representation







Experimental results
EXPERIMENTAL RESULTS

  • Biomedical data sets

    • AIMed

    • LLL

  • Newspaper articles

    • Roth and Yih

  • SEMEVAL 2007







Other approaches to re
OTHER APPROACHES TO RE

  • Using syntactic information

  • Using lexical features


Syntactic information for re
Syntactic information for RE

  • Pros:

    • more structured information useful when dealing with long-distance relations

  • Cons:

    • not always robust

    • (and not available for all languages)


Zelenko et al jmlr 2003
Zelenko et al JMLR 2003

  • TREE KERNEL defined over a shallow parse tree representation of the sentences

    • approach vulnerable to unrecoverable parsing errors

  • data set: 200 news articles (not publicly available)

  • two types of relations : person-affiliation and organization-location



Culotta sorensen 2004
CULOTTA & SORENSEN 2004

  • generalized version of Zelenko’s kernel based on dependency trees (smallest dependency tree containing the two entities of the relation)

  • a bag-of-words kernel is used to compensate syntactic errors

  • data set: ACE 2002 & 2003

  • results: syntactic information improves performance w.r.t. bag-of-words (good precision but low recall)



Evaluation campaigns for re
EVALUATION CAMPAIGNS FOR RE

  • Much of modern evaluation of methods is done by competing with other teams on evaluation campaigns like MUC and ACE

  • Modern evaluation campaigns for RE: SEMEVAL (now *SEM)

  • Interesting to look also at the problems of

    • DATA CREATION

    • EVALUATION METRICS


Semeval 2007
SEMEVAL 2007

  • 4th International Workshop on Semantic Evaluations

  • Task 04: Classification of Semantic Relations between Nominals

    • organizers: Roxana Girju, Marti Hearst, PreslavNakov, ViviNastase, Stan Szpakowicz, Peter Turney, DenizYuret

    • 14 participating teams
















Acknowledgments
ACKNOWLEDGMENTS

  • Many slides borrowed from

    • Roxana Girju

    • Alberto Lavelli


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