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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• Number of examples generated per sentence
is NC2 – 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 / Giuliano Lavelli & 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
• Pairwise inner 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 – PoS tagging
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, Preslav
Nakov, ViviNastase, Stan Szpakowicz, Peter Turney, Deniz Yuret
– 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