基于ECPA神经网络的情绪原因识别方法

Emotion Cause Recognition Based on Emotion Context Position Attention(ECPA)Neural Network

情绪原因识别是文本情绪分析领域中的一个前沿研究方向。传统情绪原因识别方法需要进行规则制定、抽取特征,而该文从情绪原因的语言特点出发,结合Bi-LSTM模型和注意力机制,提出一种基于情绪上下文位置注意力神经网络的情绪原因识别方法(ECPA)。该方法考虑了情绪词和情绪类别中的情绪信息,学习了Bi-LSTM模型建模后的上下文语义信息,引入了基于位置信息的注意力机制模型,进而构建情绪原因识别模型。实验结果证明,该方法在情绪原因识别任务中的有效性,并取得了目前最优的性能,同时对情绪归因方法具有一定的指导作用。

Emotion cause recognition is a new research issue in the field of text emotion analysis.Based on linguistic characteristic of emotion cause,we propose an emotion cause recognition method by emotion context position attention neural network(ECPA).This method considers emotion information such as emotion and category embedding,applies Bi-LSTM to capture context semantic information,and imports a position-based attention mechanism to recognize emotion cause.The experimental results demonstrate the effectiveness of our proposed model,which achieves better performance than state-of-the-art models.