基于联合学习的问答情感分类方法

An, M., Shen, C., Li, S., & Lee, Y. M. (2019). 基于联合学习的问答情感分类方法. 中文信息学报 (Journal of Chinese information processing), 33(10).

Abstract

Sentiment classification towards Question-Answering reviews is a novel and challenging task in sentiment analysis community. However, due to the limited annotation corpus for QA sentiment classification, it is difficult to achieve significant improvement via supervised approaches. To overcome this problem, we propose a joint learning approach for QA sentiment classification, which treats QA sentiment classification as the main task while traditional review sentiment classification as the auxiliary task. In detail, we first encode QA review into a sentiment vector with main task model. Then, we propose an auxiliary task model to learn auxiliary QA sentiment information representation with the help of traditional review. Finally, we update the parameters both in main task model and auxiliary task model simultaneously through joint learning. Empirical results demonstrate the impressive effectiveness of the proposed joint learning approach in contrast to a number of state-of-the-art baselines.

Link to publication in Journal of Chinese information processing