The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Open accessAbstract
Joint extraction of entities and relations is an important task in information extraction.To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem.Then, based on our tagging scheme, we study different end-toend models to extract entities and their relations directly, without identifying entities and relations separately.We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods.What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.
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Distant supervision for relation extraction without labeled data
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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947
Hogan, A., Blomqvist, E., Cochez, M., D’amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., et al. (2022) · ACM Computing Surveys · doi:10.1145/3447772 · Cited by 1,904
Luan, Y., He, L., Ostendorf, M., & Hajishirzi, H. (2018) · arXiv · doi:10.48550/arxiv.1808.09602 · Cited by 714
A Frustratingly Easy Approach for Entity and Relation Extraction
Zhong, Z., & Chen, D. (2020) · arXiv · doi:10.48550/arxiv.2010.12812 · Cited by 531
Luan, Y., He, L., Ostendorf, M., & Hajishirzi, H. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1360 · Cited by 422
Metadata
- Authors
- Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., & Xu, B.
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 1227-1236
- OpenAlex
- W2808142148
- Cited by
- 787
- References
- 34
- FWCI
- 22.36
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., & Xu, B. (2017). Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p17-1113
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