@inproceedings{911b80536d0b4d698822a5ded0d0d5c7,
title = "A composite kernel to extract relations between entities with both flat and structured features",
abstract = "This paper proposes a novel composite kernel for relation extraction. The composite kernel consists of two individual kernels: an entity kernel that allows for entity-related features and a convolution parse tree kernel that models syntactic information of relation examples. The motivation of our method is to fully utilize the nice properties of kernel methods to explore diverse knowledge for relation extraction. Our study illustrates that the composite kernel can effectively capture both flat and structured features without the need for extensive feature engineering, and can also easily scale to include more features. Evaluation on the ACE corpus shows that our method outperforms the previous best-reported methods and significantly outperforms previous two dependency tree kernels for relation extraction.",
author = "Min Zhang and Jie Zhang and Jian Su and Guodong Zhou",
year = "2006",
doi = "10.3115/1220175.1220279",
language = "英语",
isbn = "1932432655",
series = "COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "825--832",
booktitle = "COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",
address = "澳大利亚",
note = "21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 ; Conference date: 17-07-2006 Through 21-07-2006",
}