A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction

Abstract

Aspect-based sentiment analysis (ABSA) task is a fine-grained task of natural language processing and consists of two subtasks: aspect term extraction (ATE) and aspect polarity classification (APC). Most of the related works merely focus on the subtask of Chinese aspect term polarity inferring and fail to emphasize the research of Chinese-oriented ABSA multi-task learning. Based on the local context focus (LCF) mechanism, this paper firstly proposes a multi-task learning model for Chinese-oriented aspect-based sentiment analysis, namely LCF-ATEPC. Compared with other models, this model equips the capability of extracting aspect term and inferring aspect term polarity synchronously. The experimental results on four Chinese review datasets outperform state-of-the-art performance on the ATE and APC subtask. And by integrating the domain-adapted BERT model, LCF-ATEPC achieves the state-of-the-art performance of ATE and APC in the most commonly used SemEval-2014 task4 Restaurant and Laptop datasets. Moreover, this model is effective to analyze both Chinese and English reviews collaboratively and the experimental results on a multilingual mixed dataset prove its effectiveness.