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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (5): 86-92    DOI: 10.11925/infotech.2096-3467.2018.0818
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Extracting Relationship of Agricultural Financial Texts with Attention Mechanism
Yuemin Wu,Ganggui Ding,Bin Hu()
College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
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[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.

Key wordsAttention Mechanism      Relationship Extraction      Agricultural Finance     
Received: 24 July 2018      Published: 03 July 2019

Cite this article:

Yuemin Wu,Ganggui Ding,Bin Hu. Extracting Relationship of Agricultural Financial Texts with Attention Mechanism. Data Analysis and Knowledge Discovery, 2019, 3(5): 86-92.

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[1] 董静, 孙乐, 冯元勇, 等. 中文实体关系抽取中的特征选择研究[J]. 中文信息学报, 2007, 21(4): 80-91.
[1] (Dong Jing, Sun Le, Feng Yuanyong, et al.Chinese Automatic Entity Relation Extraction[J]. Journal of Chinese Information Processing, 2007, 21(4): 80-91.)
[2] 贾真, 冶忠林, 尹红风, 等. 基于Tri-training与噪声过滤的弱监督关系抽取[J]. 中文信息学报, 2016, 30(4): 142-149.
[2] (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.)
[3] 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.
[3] (Huang Beijing.Study on the Application of Deep Learning Technology in Chinese Personal Relation Extraction[D]. Shanghai: East China Normal University, 2017.)
[4] 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.
[5] 韩冰, 林鸿飞. 基于支撑向量机的人物关系抽取[C]// 第七届中文信息处理国际会议. 北京: 电子工业出版社, 2007.
[5] (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.)
[6] 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.
[7] 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.
[8] 车万翔, 刘挺, 李生. 实体关系自动抽取[J]. 中文信息学报, 2005, 19(2): 2-7.
[8] (Che Wanxiang, Liu Ting, Li Sheng.Automatic Entity Relation Extraction[J]. Journal of Chinese Information Processing, 2005, 19(2): 2-7.)
[9] 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.
[10] 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.
[11] 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.
[12] Zhang D, Wang D.Relation Classification via Recurrent Neural Network[OL]. arXiv Preprint. arXiv: 1508. 01006.
[13] 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.
[14] 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.
[15] 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.
[16] Santos C N, Xiang B, Zhou B.Classifying Relations by Ranking with Convolutional Neural Networks[OL]. arXiv Preprint. arXiv: 1504. 06580.
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