Abstract
Relation classification is an important part in natural language processing (NLP) field. The main task of relation classification is extracting the relations between target entities. In recent years, there are many methods for relation classification and some of them have achieved quite good results, but these methods have not given enough attention to the target words, and the semantic information of words is also lack of utilization. In order to make good use of the contextual information in the sentences as much as possible, we adopt the bidirectional gated recurrent unit networks (BGRU). On this basis, in order to focus on the computing process of target entities and target sentences, we add the multiple attention mechanism. Meanwhile, other semantic information such as the named entity and part of speech information of the word are also added as input data so as to make full use of the words’ information in the corpus. We have conducted some experiments on the widely used datasets, and we got up to 3% improvement in the F1 value compared to previous optimal method.
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References
Cui, H., Sun, R., Li, K., Kan, M.Y., Chua, T.S.: Question answering passage retrieval using dependency relations. In: 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 400–407. ACM Press, Salvador (2005)
Mottin, D., Lissandrini, M., Velegrakis, Y., Palpanas, T.: Exemplar queries: give me an example of what you need. Proc. VLDB Endow. 7(5), 365–376 (2014)
Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. Mach. Learn. Res. 3(Feb), 1083–1106 (2003)
Kambhatla, N.: Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations. In: ACL 2004 on Interactive Poster and Demonstration Sessions, p. 22. Association for Computational Linguistics. Barcelona (2004)
Qu, M., Ren, X., Zhang, Y., Han, J.: Weakly-supervised relation extraction by pattern-enhanced embedding learning. In: 2018 World Wide Web Conference on World Wide Web, pp. 1257–1266. Lyon (2018)
Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: the 25th International Conference on Computational Linguistics: Technical Papers, pp. 2335–2344, Dublin (2014)
Santos, C.N.D., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks. In: International Joint Conference on Natural Language Processing, pp. 626–634 (2015)
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, Lisbon (2015)
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, pp. 2124–2133. Berlin (2016)
Zhang, D., Wang, D.: Relation classification via recurrent neural network. J. Comput. Lang. arXiv:1508.01006v2 (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Xu, Y., Mou, L., Li, G., Chen, Y., Peng, H., Jin, Z.: Classifying relations via long short term memory networks along shortest dependency paths. In: 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1785–1794. Lisbon (2015)
Peng, N., Poon, H., Quirk, C., Toutanova, K., Yih, W.T.: Cross-sentence N-ary relation extraction with graph LSTMs. Transact. Assoc. Comput. Linguist. 5(1), 101–115 (2017)
Zhou, P., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: the 54th Annual Meeting of the Association for Computational Linguistics, pp. 207–212. Berlin (2016)
Cho, K., et al.: Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Empirical Methods in Natural Language Processing, pp. 1724–1734 (2014)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci., arXiv preprint arXiv:1207.0580 (2012)
Zhang, S., Zheng, D., Hu, X., Yang, M.: Bidirectional long short-term memory networks for relation classification. In: the 29th Pacific Asia Conference on Language, Information and Computation, Shanghai, pp. 73–78 (2015)
Ji, G., Liu, K., He, S., Zhao, J.: Distant supervision for relation extraction with sentence-level attention and entity descriptions. In: the 31st AAAI Conference on Artificial Intelligence, San Francisco, pp. 3060–3066 (2017)
Acknowledgments
This work was supported by the Key Projects of Science and Technology Research of Hebei Province Higher Education [ZD2017304].
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Meng, B., XU, B., Zhou, E., YU, S., Yin, H. (2019). Bidirectional Gated Recurrent Unit Networks for Relation Classification with Multiple Attentions and Semantic Information. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_14
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DOI: https://doi.org/10.1007/978-3-030-22796-8_14
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