Abstract
Joint entity and relation extraction from unstructured texts is a crucial task in natural language processing and knowledge graph construction. Recent approaches still suffer from error propagation and exposure bias because most models decompose joint entity and relation extraction into several separate modules for cooperation. In addition, the mode of multi-module cooperation to complete the joint extraction task ignores the information interaction between entities and relations. Most modeling methods are based on the pattern of token pairs, which leads to ambiguous information about entities to a certain extent. To address these issues, in this work, we creatively propose a method to transform the extraction task of complex triples under multiple relations into a fine-grained classification problem based on word pairs. Specifically, to fully utilize entity information and facilitate decoding, the proposed model uses a tag strategy specific to the feature of the entity itself. Extensive experiments show that the performance achieved by the proposed model outperforms public benchmarks and delivers consistent gain on complex scenarios of overlapping triples.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bekoulis, G., Deleu, J., Demeester, T., Develder, C.: Joint entity recognition and relation extraction as a multi-head selection problem. Expert Systems with Applications 114, 34–45 (2018)
Chan, Y.S., Roth, D.: Exploiting syntactico-semantic structures for relation extraction. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. pp. 551–560 (2011)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Dozat, T., Manning, C.D.: Deep biaffine attention for neural dependency parsing. arXiv preprint arXiv:1611.01734 (2016)
Eberts, M., Ulges, A.: Span-based joint entity and relation extraction with transformer pre-training. arXiv preprint arXiv:1909.07755 (2019)
Fu, T.J., Li, P.H., Ma, W.Y.: Graphrel: Modeling text as relational graphs for joint entity and relation extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 1409–1418 (2019)
Gardent, C., Shimorina, A., Narayan, S., Perez-Beltrachini, L.: Creating training corpora for nlg micro-planners. In: ACL (1) (2017)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lin, Y., Shen, S., Liu, Z., Luan, H., Sun, M.: Neural relation extraction with selective attention over instances. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 2124–2133 (2016)
Liu, J., Chen, S., Wang, B., Zhang, J., Li, N., Xu, T.: Attention as relation: learning supervised multi-head self-attention for relation extraction. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. pp. 3787–3793 (2021)
Ma, L., Ren, H., Zhang, X.: Effective cascade dual-decoder model for joint entity and relation extraction. arXiv preprint arXiv:2106.14163 (2021)
Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 30 (2016)
Ratinov, L., Roth, D.: Design challenges and misconceptions in named entity recognition. In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009). pp. 147–155 (2009)
Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 148–163. Springer (2010)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15(1), 1929–1958 (2014)
Wang, Y., Sun, C., Wu, Y., Zhou, H., Li, L., Yan, J.: Unire: A unified label space for entity relation extraction. arXiv preprint arXiv:2107.04292 (2021)
Wang, Y., Yu, B., Zhang, Y., Liu, T., Zhu, H., Sun, L.: Tplinker: Single-stage joint extraction of entities and relations through token pair linking. arXiv preprint arXiv:2010.13415 (2020)
Wei, Z., Su, J., Wang, Y., Tian, Y., Chang, Y.: A novel cascade binary tagging framework for relational triple extraction. arXiv preprint arXiv:1909.03227 (2019)
Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. Journal of machine learning research 3(Feb), 1083–1106 (2003)
Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 conference on empirical methods in natural language processing. pp. 1753–1762 (2015)
Zheng, H., Wen, R., Chen, X., Yang, Y., Zhang, Y., Zhang, Z., Zhang, N., Qin, B., Xu, M., Zheng, Y.: Prgc: potential relation and global correspondence based joint relational triple extraction. arXiv preprint arXiv:2106.09895 (2021)
Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., Xu, B.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers). pp. 207–212 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sun, M., Wang, L., Sheng, T., He, Z., Huang, Y. (2023). Multi-relation Word Pair Tag Space for Joint Entity and Relation Extraction. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13624. Springer, Cham. https://doi.org/10.1007/978-3-031-30108-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-30108-7_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-30107-0
Online ISBN: 978-3-031-30108-7
eBook Packages: Computer ScienceComputer Science (R0)