Introduction to artificial intelligence
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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

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

Massimo PoesioSupervised 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

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


GENERATING CANDIDATES TO CLASSIFY


RE AS A BINARY CLASSIFICATION TASK


NUMBER OF CANDIDATES TO CLASSIFY – SIMPLE MINDED VERSION


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

  • 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


MODULARITY OF KERNEL METHODS


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

Bunescu & Mooney, NIPS 2005


WORD-SEQUENCE KERNELS

  • Two families of “basic” kernels

    • Global Context

    • Local Context

  • Linear combination of kernels

  • Explicit computation

    • Extremely sparse input representation


THE GLOBAL CONTEXT KERNEL


THE GLOBAL CONTEXT KERNEL


THE LOCAL CONTEXT KERNEL


LOCAL CONTEXT KERNEL (2)


KERNEL COMBINATION


EXPERIMENTAL RESULTS

  • Biomedical data sets

    • AIMed

    • LLL

  • Newspaper articles

    • Roth and Yih

  • SEMEVAL 2007


EVALUATION METHODOLOGIES


EVALUATION (2)


EVALUATION (3)


EVALUATION (4)


RESULTS ON AIMED


OTHER APPROACHES TO RE

  • Using syntactic information

  • Using lexical features


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

  • 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


ZELENKO ET AL


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)


CULOTTA AND SORENSEN (2)


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

  • 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


SEMEVAL 2007: THE RELATIONS


SEMEVAL 2007: DATASET CREATION


SEMEVAL 2007: DATASET CREATION (2)


SEMEVAL 2007 – DATASET CREATION (3)


SEMEVAL 2007 – DATASET CREATION (4)


SEMEVAL 2007: DATASET


SEMEVAL 2007: COMPETITION


SEMEVAL 2007: COMPETITION (2)


SEMEVAL 2007: BEST RESULTS


INFLUENCE OF NER ON RE


INFLUENCE OF NER ON RE (2)


GENERATING CANDIDATES


GENERATING CANDIDATES


GENERATING CANDIDATES


ACKNOWLEDGMENTS

  • Many slides borrowed from

    • Roxana Girju

    • Alberto Lavelli


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