基于上下文和位置交互协同注意力的文本情绪原因识别

Context and Position Interactive Co-Attention Neural Network for Text Emotion Cause Detection

文本情绪原因识别是情绪分析的重要研究任务,其目的是发现文本中个体情绪产生、变迁的原因。近年来,深度神经网络和注意力机制被广泛应用到情绪原因识别方法中,取得了较好的效果。但在这些工作中,文本中的语义信息以及上下文信息未能被充分学习,子句的相对位置信息也未被有效利用。因此,该文提出一种基于上下文和位置交互的协同注意力神经网络模型(Context and Position Interactive Co-Attention Neural Network, CPC-ANN)来识别情绪原因。该模型不仅通过Transformer网络的多头自注意力机制学习到不同的文本子句语义信息,还充分利用候选原因子句的邻近子句来获得更多的上下文信息。同时,该模型通过在子句的每个词向量中嵌入相对位置信息,为文本情绪原因识别提供线索。在EMNLP2016中文情绪原因发现数据集上的实验结果显示,CPC-ANN模型取得了比其他基线模型更好的效果。

Emotion cause detection is an important research task in the field of sentiment analysis, with the purpose to find emotional cause of the individual emotion and its change in texts. To better capture the semantic information, the context information, and the relative position information of clauses in the text, this paper proposes a Context and Position Interactive Co-attention Neural Network(CPC-ANN) to detection emotion causes. CPC-ANN learn the semantic information of different text clauses through the multi-head self-attention mechanism of Transformer. At the same time, CPC-ANN embeds the relative position information into each word of clauses to provide clues for the detection of emotional causes. The experimental results on the EMNLP2016 Chinese emotion cause detection dataset show that CPC-ANN model achieves better results than the other baseline models.