Multi-granularity bidirectional attention stream machine comprehension method for emotion cause extraction

摘要

Emotion cause extraction is to extract the cause information that triggers the emotion expression of described person. This task in text plays a critical role in natural language processing applications, such as sentiment analysis and semantic comprehension system. However, most existing methods for this emotion cause extraction task only focus on feature engineering and ignore the latent semantic information between emotion word and context to hinder the performance. In this paper, we propose a novel computational multi-granularity bidirectional attention stream network based on a machine comprehension frame to settle this problem. The context and query are embedded by this multistage hierarchical process based on the fine-grained levels of embeddings. Then, the bidirectional attention stream mechanism is applied to get an emotional query-aware context representation. Meanwhile, we have conducted extensive experiments on available Chinese emotion cause dataset. The experimental results demonstrate that our approach significantly outperforms the state-of-the-art methods and is able to extract the emotion cause.