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

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
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
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
EXPERIMENTAL RESULTS
• Biomedical data sets
• AIMed
• LLL
• Newspaper articles
• Roth and Yih
• SEMEVAL 2007
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
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
• 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
ACKNOWLEDGMENTS
• Many slides borrowed from
• Roxana Girju
• Alberto Lavelli