Identifying Financial Text Causality with Bi-LSTM and Two-way CNN
Zhang Shunxiang(),Zhang Zhenjiang,Zhu Guangli,Zhao Tong,Huang Ju
School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
[Objective] This paper proposes a network model with Bi-LSTM and two-way CNN, which addresses the missing characteristic information for causality identification and improves its accuracy. [Methods] First, we used the Bi-LSTM to generate the text feature matrix for the financial texts. Then, we extracted the causal features from the matrix using two-way CNN with different convolution cores. Third, we spliced the feature vectors obtained by maximum and average pooling methods. Finally, we transferred the spliced vectors to the full connection layer for output. [Results] The accuracy of our new model reached 82.3%, which is at least 3% higher than those of the existing methods. [Limitations] We did not establish specific function module for the financial texts. [Conclusions] The proposed model could effectively identify the causality from the documents.
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doi: 10.7523/j.issn.2095-6134.2021.02.013
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