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A multi-task learning model with graph convolutional networks for aspect term extraction and polarity classification

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Abstract

The multi-task learning of aspect-based sentiment analysis aims to extract the aspect terms and predict the sentiment polarities for such terms. The majority of research has focused on improving the representations of syntactical information, while ignoring the importance of syntactic dependency. The internal relationship between the aspect terms extraction and the aspect polarity classification has not been well exploited. In this paper, we propose a unified model with Location-aware Graph Convolutional Networks (L-GCNs) for aspect-based multi-task learning. Firstly, we reconstruct graph convolutional networks by considering the location information of special aspect terms to highlight the aspect-related dependency information, which is adopted to extract aspect terms. Then, we redesign the dependency tree by considering long-distance dependencies to aggregate more context-related dependency information. Finally, combined with the dependency information, the sentiment features of special aspect terms can be captured by applying attention encoding. We evaluate the proposed model for four benchmark datasets and our experimental results show that the unified model achieves state-of-the-art performance of multi-task learning. In addition, ablation studies and different variants verify the rationality and effectiveness of our model.

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Notes

  1. https://spacy.io/

  2. http://alt.qcri.org/semeval2014/task4

  3. https://alt.qcri.org/semeval2015/task12

  4. https://nlp.stanford.edu/projects/glove

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Acknowledgements

This paper is supported by (1) the National Natural Science Foundation of China under Grant nos.61672179, 61370083, (2) the Project funded by China Postdoctoral Science Foundation under Grant no.2019M651262, (3) the Youth Fund Project of Humanities and Social Sciences Research of the Ministry of Education of China under Grant no.20YJCZH172.

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  1. Harbin Engineering University, Harbin, Heilongjiang, 150001, China

    Meng Zhao, Jing Yang & Lianwei Qu

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  1. Meng Zhao
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Correspondence to Jing Yang.

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Zhao, M., Yang, J. & Qu, L. A multi-task learning model with graph convolutional networks for aspect term extraction and polarity classification. Appl Intell 53, 6585–6603 (2023). https://doi.org/10.1007/s10489-022-03573-6

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