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

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


Generating candidates to classify

GENERATING CANDIDATES TO CLASSIFY


Re as a binary classification task

RE AS A BINARY CLASSIFICATION TASK


Number of candidates to classify simple minded version

NUMBER OF CANDIDATES TO CLASSIFY – SIMPLE MINDED VERSION


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


Modularity of kernel methods

MODULARITY OF KERNEL METHODS


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


The global context kernel

THE GLOBAL CONTEXT KERNEL


The global context kernel1

THE GLOBAL CONTEXT KERNEL


The local context kernel

THE LOCAL CONTEXT KERNEL


Local context kernel 2

LOCAL CONTEXT KERNEL (2)


Kernel combination

KERNEL COMBINATION


Experimental results

EXPERIMENTAL RESULTS

  • Biomedical data sets

    • AIMed

    • LLL

  • Newspaper articles

    • Roth and Yih

  • SEMEVAL 2007


Evaluation methodologies

EVALUATION METHODOLOGIES


Evaluation 2

EVALUATION (2)


Evaluation 3

EVALUATION (3)


Evaluation 4

EVALUATION (4)


Results on aimed

RESULTS ON AIMED


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


Zelenko et al

ZELENKO ET AL


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)


Culotta and sorensen 2

CULOTTA AND SORENSEN (2)


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


Semeval 2007 the relations

SEMEVAL 2007: THE RELATIONS


Semeval 2007 dataset creation

SEMEVAL 2007: DATASET CREATION


Semeval 2007 dataset creation 2

SEMEVAL 2007: DATASET CREATION (2)


Semeval 2007 dataset creation 3

SEMEVAL 2007 – DATASET CREATION (3)


Semeval 2007 dataset creation 4

SEMEVAL 2007 – DATASET CREATION (4)


Semeval 2007 dataset

SEMEVAL 2007: DATASET


Semeval 2007 competition

SEMEVAL 2007: COMPETITION


Semeval 2007 competition 2

SEMEVAL 2007: COMPETITION (2)


Semeval 2007 best results

SEMEVAL 2007: BEST RESULTS


Influence of ner on re

INFLUENCE OF NER ON RE


Influence of ner on re 2

INFLUENCE OF NER ON RE (2)


Generating candidates

GENERATING CANDIDATES


Generating candidates1

GENERATING CANDIDATES


Generating candidates2

GENERATING CANDIDATES


Acknowledgments

ACKNOWLEDGMENTS

  • Many slides borrowed from

    • Roxana Girju

    • Alberto Lavelli


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