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Incremental Joint Extraction of Entity Mentions and Relations
Open accessAbstract
We present an incremental joint framework to simultaneously extract entity mentions and relations using structured perceptron with efficient beam-search.A segment-based decoder based on the idea of semi-Markov chain is adopted to the new framework as opposed to traditional token-based tagging.In addition, by virtue of the inexact search, we developed a number of new and effective global features as soft constraints to capture the interdependency among entity mentions and relations.Experiments on Automatic Content Extraction (ACE) 1 corpora demonstrate that our joint model significantly outperforms a strong pipelined baseline, which attains better performance than the best-reported end-to-end system.
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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
A Frustratingly Easy Approach for Entity and Relation Extraction
Zhong, Z., & Chen, D. (2020) · arXiv · doi:10.48550/arxiv.2010.12812 · Cited by 531
A Comprehensive Survey on Automatic Knowledge Graph Construction
Zhong, L., Wu, J., Li, Q., Peng, H., & Wu, X. (2024) · ACM Computing Surveys · doi:10.1145/3618295 · Cited by 382
Self-citationModeling Joint Entity and Relation Extraction with Table Representation
Miwa, M., & Sasaki, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1200 · Cited by 369
Entity-Relation Extraction as Multi-Turn Question Answering
Li, X., Yin, F., Sun, Z., Li, X., Yuan, A., Chai, D., Zhou, M., & Li, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1129 · Cited by 336
Metadata
- Authors
- Li, Q., & Ji, H.
- Venue
- Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2014
- Pages
- 402-412
- OpenAlex
- W2134033474
- Cited by
- 535
- References
- 0
- FWCI
- 15.22
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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