融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取

Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM

金融领域的文本信息量大、价值高,尤其是其中的隐式因果关系事件包含着巨大的潜在利用价值。对金融领域文本进行隐式因果关系分析,挖掘隐式因果关系事件中隐含的重要信息,了解金融领域事件更深层的演化逻辑,进而构建金融领域知识库,对金融风险控制、风险预警等具有重要意义。为了提高金融领域中隐式因果关系事件识别的准确度,从特征挖掘的角度入手,提出了一种基于自注意力机制的融合循环注意力卷积神经网络(Recurrent Attention Convolution Neural Network,RACNN)和双向长短时记忆网络(Bidirectional Long Short-Term Memory,BiLSTM)的隐式因果关系抽取方法。该方法结合了基于迭代反馈机制能提取更重要文本局部特征的RACNN、能更好地提取文本全局特征的BiLSTM以及能更深入地挖掘融合特征语义信息的自注意力机制,在SemEval-2010 Task 8数据集和金融领域数据集上进行了实验,结果表明,评估指标F1值分别达到了72.98%和75.74%,均显著优于其他对比模型。

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.