Multi-task learning for aspect term extraction and aspect sentiment classification

Md Shad Akhtar

doi:10.1016/j.neucom.2020.02.093

Multi-task learning for aspect term extraction and aspect sentiment classification

2020-07-01

80

PRE

AI

DOI：10.1016/j.neucom.2020.02.093

Original

Original request for help

Save

Abstract

En 中文

Aspect sentiment classification has a dependency over the aspect term extraction. The majority of the existing studies tackle these two problems independently, i.e., while performing aspect sentiment classification, it is assumed that the aspect terms are pre-identified. However, such assumptions are neither practical nor appropriate. In this paper, we address these impractical limitations and propose a multitask learning framework for the identification and classification of aspect terms in a unified model. At first, the proposed approach employs a BiLSTM followed by a self-attention mechanism to identify the aspect terms in a given sentence. Subsequently, the architecture utilizes a CNN framework to predict the sentiments of the identified aspect terms. We evaluate our proposed approach for the three benchmark datasets across two languages, i.e., English and Hindi. Experimental results suggest that the proposed multi-task model achieves competitive performance with reduced complexity (i.e., a single model for the two tasks compared to two separate models for each task) for both the languages. (C) 2020 Elsevier B.V. All rights reserved.

Keywords:

Aspect based sentiment analysis

Aspect term extraction

Aspect sentiment classification

Multi-task learning

Joint-modelling

Multi-task learning for aspect term extraction and aspect sentiment classification

Abstract

AI Summary

Journal

Organization

Cited Papers