Abstract
Distant supervised relation extraction has been widely used to extract relational facts in large-scale corpus but inevitably suffers from the wrong label problem. Many methods use attention mechanisms to address this issue. However, the attention weights in these models are not discriminative and precise enough to fully filter out noise. In this paper, we propose a novel Pattern Filtering Attention (PFA), which can filter noise effectively. Firstly, we adopt an online clustering algorithm on the instances labeled with the same relation to extract potential semantic centers (positive patterns) of each relation, and these patterns have less noise statistically. Then, we build a sentence-level attention based on the similarities of instances and positive patterns. Due to the large differences between these similarities, our model can assign more discriminative weights to instances to reduce the influence of noisy data. Experimental results on the New York Times (NYT) dataset show that our model can effectively improve the performance of relation extraction compared with state-of-the-art methods.
This material is supported partially by National Key R&D Program of China under Grant No. 2018YFC1604000 and No. 2018YFC1604003, partially by National Science Foundation of China (NSFC) under Grant No. 61872272 and No. 61772382.
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References
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. AcM (2008)
Du, J., Han, J., Way, A., Wan, D.: Multi-level structured self-attentions for distantly supervised relation extraction. In: EMNLP (2018)
Duda, R.: Sequential k-means clustering. http://www.cs.princeton.edu/courses/archive/fall08/cos436/Duda/C/sk_means.htm
Feng, J., Huang, M., Zhao, L., Yang, Y., Zhu, X.: Reinforcement learning for relation classification from noisy data. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
GuoDong, Z., Jian, S., Jie, Z., Min, Z.: Exploring various knowledge in relation extraction. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 427–434. Association for Computational Linguistics (2005)
Han, X., Yu, P., Liu, Z., Sun, M., Li, P.: Hierarchical relation extraction with coarse-to-fine grained attention. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2236–2245 (2018)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)
Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L., Weld, D.S.: Knowledge-based weak supervision for information extraction of overlapping relations. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 541–550. Association for Computational Linguistics (2011)
Ji, G., Liu, K., He, S., Zhao, J., et al.: Distant supervision for relation extraction with sentence-level attention and entity descriptions. In: AAAI, pp. 3060–3066 (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, T., Wang, K., Chang, B., Sui, Z.: A soft-label method for noise-tolerant distantly supervised relation extraction. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1790–1795 (2017)
Luo, B., et al.: Learning with noise: enhance distantly supervised relation extraction with dynamic transition matrix. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 430–439 (2017)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 2, pp. 1003–1011. Association for Computational Linguistics (2009)
Mooney, R.J., Bunescu, R.C.: Subsequence kernels for relation extraction. In: Advances in Neural Information Processing Systems, pp. 171–178 (2006)
Peng, M., et al.: Improving distant supervision of relation extraction with unsupervised methods. In: Cellary, W., Mokbel, M.F., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds.) WISE 2016. LNCS, vol. 10041, pp. 561–568. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48740-3_42
Qin, P., Xu, W., Wang, W.Y.: DSGAN: generative adversarial training for distant supervision relation extraction. arXiv preprint arXiv:1805.09929 (2018)
Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6323, pp. 148–163. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15939-8_10
Surdeanu, M., Tibshirani, J., Nallapati, R., Manning, C.D.: Multi-instance multi-label learning for relation extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 455–465. Association for Computational Linguistics (2012)
Takamatsu, S., Sato, I., Nakagawa, H.: Reducing wrong labels in distant supervision for relation extraction. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, pp. 721–729. Association for Computational Linguistics (2012)
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)
Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 2335–2344 (2014)
Zhang, N., Deng, S., Sun, Z., Chen, X., Zhang, W., Chen, H.: Attention-based capsule networks with dynamic routing for relation extraction. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 986–992 (2018)
Zhang, Y., Qi, P., Manning, C.D.: Graph convolution over pruned dependency trees improves relation extraction. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2205–2215 (2018)
Zhou, P., et al.: 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)
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Peng, M., Liao, Q., Hu, W., Tian, G., Wang, H., Zhang, Y. (2019). Pattern Filtering Attention for Distant Supervised Relation Extraction via Online Clustering. In: Cheng, R., Mamoulis, N., Sun, Y., Huang, X. (eds) Web Information Systems Engineering – WISE 2019. WISE 2020. Lecture Notes in Computer Science(), vol 11881. Springer, Cham. https://doi.org/10.1007/978-3-030-34223-4_20
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