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DL '00 Proceedings of the fifth ACM conference on Digital libraries
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WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
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The Journal of Machine Learning Research
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COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
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ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Training conditional random fields with multivariate evaluation measures
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
StatSnowball: a statistical approach to extracting entity relationships
Proceedings of the 18th international conference on World wide web
Acquiring Semantic Relations Using the Web for Constructing Lightweight Ontologies
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Detecting semantic relations between named entities in text using contextual features
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Japanese dependency parsing using sequential labeling for semi-spoken language
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
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This paper proposes a supervised learning method to recognize expressions that show a relation between two named entities, e.g., person, location, or organization. The method uses two novel features, 1) whether the candidate words inherently express relations and 2) how the candidate words are influenced by the past relations of two entities. These features together with conventional syntactic and contextual features are organized as a tree structure and are fed into a boosting-based classification algorithm. Experimental results show that the proposed method outperforms conventional methods.