The DOI matches its Crossref record.
A composite kernel to extract relations between entities with both flat and structured features
Open accessAbstract
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 out-performs previous two dependency tree kernels for relation extraction.
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Citations
Cited by
Sarawagi, S. (2008) · Foundations and Trends in Databases · doi:10.1561/1900000003 · Cited by 707
Relation Extraction: Perspective from Convolutional Neural Networks
Nguyen, T. H., & Grishman, R. (2015) · Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing · doi:10.3115/v1/w15-1506 · Cited by 486
Introduction to “This is Watson”
Ferrucci, D. A. (2012) · IBM Journal of Research and Development · doi:10.1147/jrd.2012.2184356 · Cited by 484
A comprehensive survey on relation extraction: recent advances and new frontiers
Zhao, X., Deng, Y., Yang, M., Wang, L., Zhang, R., Cheng, H., Lam, W., Shen, Y., & Xu, R. (2024) · ACM Computing Surveys · doi:10.1145/3674501 · Cited by 168
“There are three main metrics [151]: precision (P), recall (R), and F1 score.”
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
- Zhang, M., Zhang, J., Su, J., & Zhou, G.
- Venue
- Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06
- Published
- 2006
- Pages
- 825-832
- OpenAlex
- W2152269015
- Cited by
- 242
- References
- 0
- FWCI
- 11.29
- Type
- conference paper
- Language
- en
- Open access
- gold
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- CoChat. (2026). Citation check: A composite kernel to extract relations between entities with both flat and structured features. https://app.cochat.ai/papers/10.3115/1220175.1220279
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- Zhang, M., Zhang, J., Su, J., & Zhou, G. (2006). A composite kernel to extract relations between entities with both flat and structured features. Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06. https://doi.org/10.3115/1220175.1220279
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
