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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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