Label Correction Model for Aspect-based Sentiment Analysis

Proceedings of the 28th International Conference on Computational Linguistics, pages 822–832 Barcelona, Spain (Online), December 8-13, 2020 822 Label Correction Model for Aspect-based Sentiment Analysis Qianlong Wang Jiangtao Ren∗ School of Data and Computer Science Guangdong Province Key Lab of Computational Science Sun Yat-sen University, China wangqlong3@mail3.sysu.edu.cn, issrjt@mail.sysu.edu.cn Abstract Aspect-based sentiment analysis includes opinion aspect extraction and aspect sentiment classi- fication. Researchers have attempted to discover the relationship between these two sub-tasks and have proposed the joint model for solving aspect-based sentiment analysis. However, they ignore a phenomenon: aspect boundary label and sentiment label of the same word can correct each other. To exploit this phenomenon, we propose a novel deep learning model named the label correction model. Specifically, given an input sentence, our model first predicts the as- pect boundary label sequence and sentiment label sequence, then re-predicts the aspect boundary (sentiment) label sequence using the embeddings of the previously predicted sentiment (aspect boundary) label. The goal of the re-prediction operation (can be repeated multiple times) is to use the information of the sentiment (aspect boundary) label to correct the wrong aspect bound- ary (sentiment) label. Moreover, we explore two ways of using label embeddings: add and gate mechanism. We evaluate our model on three benchmark datasets. Experimental results verify that our model achieves state-of-the-art performance compared with several baselines. 1 Introduction Aspect-based sentiment analysis (ABSA) aims to extract the opinion aspects mentioned in the sentence and to predict the sentiment of each aspect (Pontiki et al., 2014). For example, in the review “Average to good thai food , but terrible delivery .”, thai food and delivery are aspects, with positive and negative as their corresponding sentiments. In the literature, ABSA is usually divided into two sub-tasks, namely, opinion aspect extraction and aspect sentiment classification. The goal of opinion aspect extraction is to extract all aspects present in a sentence. Early work (Zhuang et al., 2006) focuses on detecting the pre-defined aspects in a sentence. Then, some work (Jakob and Gurevych, 2010; Liu et al., 2015) regards the opinion aspect extraction as a sequence labeling task. The goal of aspect sentiment classification is to predict the sentiment of the given opinion aspect, and this sub-task has drawn growing research interests over the past few years (Ma et al., 2017; Wang et al., 2018; Li et al., 2019b). However, most previous work focuses on only one of the sub-tasks. This limits the practical application of ABSA. To apply the existing methods of two sub-tasks in practical applications (i.e., not only extracting aspects but also predicting their sentiments), the most common way is to pipeline the methods of two sub-tasks together. To further promote the resolution of ABSA, researchers have attempted to discover the relationship between these two sub-tasks and have developed the joint models (Mitchell et al., 2013; Zhang et al., 2015). They utilize a set of aspect boundary labels (e.g., B, I, E, S, O) and a set of sentiment labels (e.g., POS, NEG, NEU and O) to make …

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