基于联合学习的问答情感分类方法

面向问答型评论的情感分类在情感分析领域是一项新颖且极具挑战性的研究任务。由于问答型评论情感分类标注数据非常匮乏,基于监督学习的情感分类方法的性能有一定限制。为了解决上述困境,该文提出了一种基于联合学习的问答情感分类方法。该方法通过大量自然标注普通评论辅助问答情感分类任务,将问答情感分类作为主任务,将普通评论情感分类作为辅助任务。具体而言,首先通过主任务模型单独学习问答型评论的情感信息;其次,使用问答型评论和普通评论共同训练辅助任务模型,以获取问答型评论的辅助情感信息;最后通过联合学习同时学习和更新主任务模型及辅助任务模型的参数。实验结果表明,基于联合学习的问答情感分类方法能较好融合问答型评论和普通评论的情感信息,大幅提升问答情感分类任务的性能。

Joint Learning for Sentiment Classification Towards Question-Answering Reviews

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.