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Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations
Open accessAbstract
Extracting semantic relationships between entities is challenging because of a paucity of annotated data and the errors induced by entity detection modules. We employ Maximum Entropy models to combine diverse lexical, syntactic and semantic features derived from the text. Our system obtained competitive results in the Automatic Content Extraction (ACE) evaluation. Here we present our general approach and describe our ACE results.
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Cited by
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., & Xu, B. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1113 · Cited by 787
Matching the Blanks: Distributional Similarity for Relation Learning
Baldini Soares, L., FitzGerald, N., Ling, J., & Kwiatkowski, T. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1279 · Cited by 731
Extracting relations with integrated information using kernel methods
Zhao, S., & Grishman, R. (2005) · Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05 · doi:10.3115/1219840.1219892 · Cited by 313
Named Entity Recognition and Relation Extraction: State-of-the-Art
Nasar, Z., Jaffry, S. W., & Malik, M. K. (2022) · ACM Computing Surveys · doi:10.1145/3445965 · Cited by 245
Modifying Memories in Transformer Models
Zhu, C., Rawat, A. S., Zaheer, M., Bhojanapalli, S., Li, D., Yu, F., & Kumar, S. (2020) · arXiv · doi:10.48550/arxiv.2012.00363 · Cited by 48
Metadata
- Authors
- Kambhatla, N.
- Venue
- Proceedings of the ACL 2004 on Interactive poster and demonstration sessions -
- Published
- 2004
- Pages
- 22-es
- OpenAlex
- W2030408698
- Cited by
- 607
- References
- 0
- FWCI
- 9.76
- Type
- conference paper
- Language
- en
- Open access
- gold
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