The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Extracting relations with integrated information using kernel methods
Open accessAbstract
Entity relation detection is a form of information extraction that finds predefined relations between pairs of entities in text. This paper describes a relation detection approach that combines clues from different levels of syntactic processing using kernel methods. Information from three different levels of processing is considered: tokenization, sentence parsing and deep dependency analysis. Each source of information is represented by kernel functions. Then composite kernels are developed to integrate and extend individual kernels so that processing errors occurring at one level can be overcome by information from other levels. We present an evaluation of these methods on the 2004 ACE relation detection task, using Support Vector Machines, and show that each level of syntactic processing contributes useful information for this task. When evaluated on the official test data, our approach produced very competitive ACE value scores. We also compare the SVM with KNN on different kernels.
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Cites
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Dependency tree kernels for relation extraction
Culotta, A., & Sorensen, J. (2004) · Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04 · doi:10.3115/1218955.1219009 · Cited by 814
Foundations of statistical natural language processing
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Kernel methods for relation extraction
Zelenko, D., Aone, C., & Richardella, A. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118703 · Cited by 573
Cited by
Yang, X., Bian, J., Gong, Y., Hogan, W. R., & Wu, Y. (2019) · Drug Safety · doi:10.1007/s40264-018-0761-0 · Cited by 66
Medical Relation Extraction with Manifold Models
Wang, C., & Fan, J. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1078 · Cited by 40
Metadata
- Authors
- Zhao, S., & Grishman, R.
- Venue
- Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05
- Published
- 2005
- Pages
- 419-426
- OpenAlex
- W2146191280
- Cited by
- 313
- References
- 13
- FWCI
- 14.79
- Type
- conference paper
- Language
- en
- Open access
- bronze
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