Attention-based Bi-LSTM-CRF Network for Emotion Cause Extraction in Texts

Emotion cause extraction is to identify the causes behind an emotion expressed in a document, a more challenging task for the fine-grained emotion analysis in natural language processing. Most existing methods regard the task as an independent clause classification problem, ignoring the relationships among multiple clauses in the same document. Moreover, the relative position of the candidate clause and emotion clause provide critical emotion cause clue. In the paper, an attention-based Bi-LSTM-CRF network is proposed to integrate the above information. In this network, a bi-directional long short-term memory is first used to capture both the contextual information and the latent semantic relations of emotion expression and candidate clause. Then, two attention mechanisms are designed to encode the mutual influence of the emotion expression and candidate clause, the relative position and candidate clause. Better-distributed representations are created with the former design. Finally, these representations are into the Condition Random Fields for labeling. The results experimented on a benchmark Chinese emotion cause dataset proved the effectiveness of our method by achieving the F score of 88.40 %.