Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs

Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks Guimin Chen~⇤, Yuanhe Tian♥⇤, Yan Song♠~† ~Shenzhen Research Institute of Big Data ♥University of Washington ♠The Chinese University of Hong Kong (Shenzhen) ~chenguimin@sribd.cn ♥yhtian@uw.edu ♠songyan@cuhk.edu.cn Abstract End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first extracts the aspect terms in a sentence and the second predicts the sentiment polarities for such terms. For EASA, compared to pipeline and multi-task approaches, joint aspect extraction and sentiment analysis provides a one-step solution to predict both aspect terms and their sentiment polarities through a single decoding process, which avoids the mismatches in between the results of aspect terms and sentiment polarities, as well as error propagation. Previous studies, especially recent ones, for this task focus on using powerful encoders (e.g., Bi-LSTM and BERT) to model contextual information from the input, with limited efforts paid to using advanced neural architectures (such as attentions and graph convolutional networks) or leveraging extra knowledge (such as syntactic information). To extend such efforts, in this paper, we propose direc- tional graph convolutional networks (D-GCN) to jointly perform aspect extraction and sentiment analysis with encoding syntactic information, where dependency among words are integrated into our model to enhance its ability to represent input sentences and help EASA accordingly. Experimental results on three benchmark datasets demonstrate the effectiveness of our approach, where D-GCN achieves state-of-the-art performance on all datasets.1 1 Introduction End-to-end aspect-based sentiment analysis (EASA) aims to extract aspect terms in the text and predict their sentiment polarities so as to understand targeted sentiment towards particular objects. For example, in the sentence “The ambiance is minimal but food is not phenomenal”, the aspect terms are “ambiance” and “food” and the sentiment polarities towards them are positive and negative, respectively. In general, there are mainly three types of approaches for this task, i.e., pipeline, multi-task, and joint-label approaches. Pipeline approaches (Mitchell et al., 2013; Zhang et al., 2015; Hu et al., 2019) perform aspect extraction and sentiment analysis in a sequence, which is not straightforward and suffers from error propagation among different steps; multi-task approaches (Mitchell et al., 2013; Zhang et al., 2015; Ma et al., 2018; Luo et al., 2019; He et al., 2019; Hu et al., 2019) apply an encoder to the input and use a separate decoding process to extract aspects and predict their sentiments, where there could be mismatches between the two decoding results. As a comparison, joint-label approaches (Mitchell et al., 2013; Zhang et al., 2015; Li and Lu, 2017; Li et al., 2019a; Hu et al., 2019) extract aspect terms and predict their sentiments simultaneously through a unified labeling scheme, which not only provides an one-step solution to EASA, but also avoids the aforementioned problems in the other two approaches. In most cases, previous studies demonstrate that a good modeling of contextual information is effective in improving EASA performance. However, these studies mainly rely on powerful encoders (e.g., Bi-LSTM, CNN, BERT) (Zhang et al., 2015; Ma et al., 2018; Schmitt et …

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