基于SoftLexicon和注意力机制的中文因果关系抽取

Chinese Causality Extraction Based on SoftLexicon and Attention Mechanism

针对现有中文因果关系抽取方法对因果事件边界难以识别和文本特征表示不充分的问题,该文提出了一种基于外部词汇信息和注意力机制的中文因果关系抽取模型BiLSTM-TWAM+CRF。该模型使用SoftLexicon方法引入外部词汇信息构建词集,解决了因果事件边界难以识别的问题。通过构建的双路关注模块TWAM(Two Way Attention Module),实现了从局部和全局两个角度充分刻画文本特征。实验结果表明,与当前中文因果关系抽取模型相比较,该文所提方法表现出更优的抽取效果。

Existing Chinese causality extraction methods is challenged by the issue of causal event boundaries identifi-cation and inadequate text features representation.This paper proposes a Chinese causality extraction model BiL-STM-TWAM+CRF which ustilizes external lexical information and attention mechanism.We introduce the external lexical information by using the SoftLexicon to construct word set for determining causal event boundaries.We con-structe a Two Way Attention Module(TWAM)to represent text features from both the local and global views.Ex-perimental results show that our proposed method has better performance than the existing Chinese causality extrac-tion methods.