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 avoid 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 directional graph convolutional networks (D-GCN) to jointly perform aspect extraction and sentiment analysis with encoding syntactic information, where dependency among words are integrated in our model to enhance its ability of representing 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.
Highlights
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
directional graph convolutional networks (D-graph convolutional networks (GCN)) works well with both base and large BERT, where consistent improvement is observed over the baselines across datasets
One possible explanation could be that we only model the contextual features directly linked to a specific word in each D-GCN layer, contextual information in the larger range can be leveraged indirectly across layers when the number of D-GCN layers increases, so that EASA performance is improved
Summary
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. These studies mainly rely on powerful encoders (e.g., Bi-LSTM, CNN, BERT) (Zhang et al, 2015; Ma et al, 2018; Schmitt et al, 2018; Li et al, 2019a; Li et al, 2019b; Luo et al, 2019; He et al, 2019; Hu et al, 2019) and pre-trained embedings (e.g., GloVe, word2vec, FastText) (Schmitt et al, 2018; Li et al, 2019a) to learn contextual information, with limited effort paid to leveraging advanced architectures and extra knowledge for this task To extend such effort, graph convolutional networks (GCN) was proposed and shows its effectiveness in conventional sentiment analysis (Zhang et al, 2019; Sun et al, 2019), as well as other tasks, e.g., text classification (Kipf and Welling, 2016), neural machine translation (Bastings et al, 2017), semantic role labeling (Marcheggiani and Titov, 2017), etc. To illustrate the effectiveness of our approach, experiments are performed on three benchmark datasets, where the results confirm that D-GCN is an appropriate model in leveraging dependency-based word relations for EASA, with state-of-theart performance observed on all datasets
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