COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Modeling commonality among related classes in relation extraction
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A composite kernel to extract relations between entities with both flat and structured features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Extracting relation information from text documents by exploring various types of knowledge
Information Processing and Management: an International Journal
Structure and semantics for expressive text kernels
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Information Processing and Management: an International Journal
A novel feature-based approach to Chinese entity relation extraction
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Exploiting constituent dependencies for tree kernel-based semantic relation extraction
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Tree kernel-based semantic relation extraction with rich syntactic and semantic information
Information Sciences: an International Journal
ACM Transactions on Asian Language Information Processing (TALIP)
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Lexical semantic information plays an important role in semantic relation extraction between named entities. This paper incorporates two kinds of lexical semantic similarity measures, thesaurus-based and corpus-based, into convolution tree kernels and systematically investigates their effects on Chinese relation extraction. The experiments on the ACE2005 Chinese corpus shows that the incorporation of lexical semantic similarity into tree kernel-based Chinese relation extraction can significantly improve the extraction performance when entity types are unknown, while in the case of known entity types, these lexical similarity measures also enhance the extraction performance for some person-related relationships. This demonstrates the usefulness of lexical semantic similarity in Chinese relation extraction.