A Multitask Multiview Neural Network for End-to-End Aspect-Based Sentiment Analysis

Big Data Mining and Analytics

Bie Yong¹  Yang Yan¹ 

[1] School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China;

关键词: deep learning;    multitask learning;    multiview learning;    natural language processing;    aspect-based sentiment analysis;   

DOI  :  10.26599/BDMA.2021.9020003

来源: DOAJ

【 摘 要 】

The aspect-based sentiment analysis (ABSA) consists of two subtasks'aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies some problems in performance and real application. This study investigates the end-to-end ABSA and proposes a novel multitask multiview network (MTMVN) architecture. Specifically, the architecture takes the unified ABSA as the main task with the two subtasks as auxiliary tasks. Meanwhile, the representation obtained from the branch network of the main task is regarded as the global view, whereas the representations of the two subtasks are considered two local views with different emphases. Through multitask learning, the main task can be facilitated by additional accurate aspect boundary information and sentiment polarity information. By enhancing the correlations between the views under the idea of multiview learning, the representation of the global view can be optimized to improve the overall performance of the model. The experimental results on three benchmark datasets show that the proposed method exceeds the existing pipeline methods and end-to-end methods, proving the superiority of our MTMVN architecture.

【 授权许可】

Unknown