Computer Science ›› 2022, Vol. 49 ›› Issue (7): 179-186.doi: 10.11896/jsjkx.210500190

• Artificial Intelligence • Previous Articles     Next Articles

Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM

JIN Fang-yan1, WANG Xiu-li1,2   

  1. 1 College of Information,Central University of Finance and Economics,Beijing 102206,China
    2 Engineering Research Center of State Financial Security,Ministry of Education,Beijing 102206,China
  • Received:2021-05-26 Revised:2021-10-18 Online:2022-07-15 Published:2022-07-12
  • About author:JIN Fang-yan,born in 1998,postgra-duate.His main research interests include financial technology and natural language processing.
    WANG Xiu-li,born in 1977,Ph.D,professor,is a senior member of China Computer Federation.His main research interests include financial technology,artificialintelligence and security.

Abstract: The financial field has a large amount of information and high value,especially the implicit causal events which contains huge potential useful value.Carrying out causal analysis on financial domain text to mine the important information hidden in the implicit causal events,understanding the deeper evolutionary logic of the financial field events,to build a financial field knowledge base,which plays an important role in financial risk control and risk early warning.In order to improve the accuracy of identifying the implicit causal events in the financial field,from the perspective of feature mining,based on self-attention mechanism,an implicit causality extraction method integrating recurrent attention convolution neural network(RACNN) and bidirectional long short-term memory(BiLSTM) is proposed.This method combines RACNN that can extract more important local features of text based on an iterative feedback mechanism,BiLSTM that can better extract global features of text,and a self-attention mechanism that can more deeply dig the semantic information of fused features.Experimental results on SemEval-2010 Task 8 and financial field datasets show that the evaluation index F1 value can reach 72.98% and 75.74% respectively,which is significantly better than other comparison models.

Key words: BiLSTM, Financial field, Implicit causality extraction, Iterative feedback mechanism, RACNN, Self-attention mechanism

CLC Number: 

  • TP391
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