Extracting Chinese Explanatory Expressions with Discrete and Neural CRFs

Recent work on opinion mining typically focuses on subtasks such as aspect mining or polarity classification, ignoring the detailed explanatory evidences that account for one certain user opinion. In this paper, we study the extraction of explanatory expressions, by modeling the problem based on conditional random field (CRF). We compare the effectiveness of both discrete and neural features, and further integrate them.We evaluate the models on two datasets from two different domains which have been annotated with ground-truth explanatory expression.Results show that the neural CRF model performs better than the discrete CRF. After a combination of the discrete and neural features, our final CRF mode achieves the top-performing results.