[Objective] This paper proposes a new method to extract relations from Chinese texts automatically. [Methods] We retrieved annual reports of 224 listed agricultural companies from 2015 to 2017. Then we adopted the Gated Recurrent Unit algorithm based on double attention mechanism to extract the needed data. [Results] The average accuracy of our model on the agricultural financial dataset reached 78%. Compared with the Recurrent Neural Network algorithm, the average accuracy of the new model increased by about 12%. [Limitations] We only studied data from 224 companies, which needs to be expanded. [Conclusions] The proposed model can effectively extract relationship from agricultural financial texts.
(Jia Zhen, Ye Zhonglin, Yin Hongfeng, et al.Weakly Supervised Relation Extraction Based on Tri-training and Noise Filtering[J]. Journal of Chinese Information Processing, 2016, 30(4): 142-149.)
黄蓓静. 深度学习技术在中文人物关系抽取中的应用研究[D]. 上海:华东师范大学, 2017.
(Huang Beijing.Study on the Application of Deep Learning Technology in Chinese Personal Relation Extraction[D]. Shanghai: East China Normal University, 2017.)
Culotta A, McCallum A, Betz J. Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text[C]// Proceedings of the Main Conference on Human Language Technology. 2006: 296-303.
(Han Bing, Lin Hongfei.Characters Extraction Based on Support Vector Machine[C]// Proceedings of the 7th International Conference on Chinese Information Processing. Beijing: Publishing House of Electronics Industry, 2007.)
Zhao S, Grishman R.Extracting Relations with Integrated Information Using Kernel Methods[C]// Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. 2005: 419-426.
Bunescu R C, Mooney R J.Subsequence Kernels for Relation Extraction[C]// Proceedings of the 2006 International Conference on Neural Information Processing Systems. MIT Press, 2006: 171-178.
(Che Wanxiang, Liu Ting, Li Sheng.Automatic Entity Relation Extraction[J]. Journal of Chinese Information Processing, 2005, 19(2): 2-7.)
Mintz M, Bills S, Snow R, et al.Distant Supervision for Relation Extraction Without Labeled Data[C]// Proceedings of the 4th International Joint Conference on Natural Language Processing of the AFNLP. 2009: 1003-1011.
Socher R, Huval B, Manning C D, et al.Semantic Compositionality Through Recursive Matrix-Vector Spaces[C]// Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2012: 1201-1211.
Zeng D, Liu K, Lai S, et al.Relation Classification via Convolutional Deep Neural Network[C]// Proceedings of the 25th International Conference on Computational Linguistics. 2014: 2335-2344.
Zhang D, Wang D.Relation Classification via Recurrent Neural Network[OL]. arXiv Preprint. arXiv: 1508. 01006.
Zhou P, Shi W, Tian J, et al.Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016: 207-212.
Lin Y, Shen S, Liu Z, et al.Neural Relation Extraction with Selective Attention over Instances[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016: 2124-2133.
Cho K, Van Merrienboer B, Gulcehre C, et al.Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation[OL]. arXiv Preprint. arXiv: 1406. 1078.
Santos C N, Xiang B, Zhou B.Classifying Relations by Ranking with Convolutional Neural Networks[OL]. arXiv Preprint. arXiv: 1504. 06580.