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ES-ASTE: enhanced span-level framework for aspect sentiment triplet extraction

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Abstract

Aspect sentiment triplet extraction is an important research in the field of sentiment analysis, aiming at extracting aspect, opinion expression, and aspect-based sentiment at once. Most of existing end-to-end methods are tagging-based, which neglect the interaction between aspect term and opinion term while predicting the sentiment polarity. Besides, these methods cannot accurately resolve the complex correspondence between aspect terms and opinion terms. To address above concerns, we propose a span-level framework to extract aspect sentiment triplets, which is enhanced by introducing syntactic dependency relation and part-of-speech combination features through graph convolutional networks to deal with the complex relationships between aspect terms and opinion terms. Moreover, the proposed model explicitly considers the interaction between aspect and opinion spans generated by dual-channel pruning strategy when predicting their sentiment polarity. Experimental results demonstrate that our framework achieves strong performance over the baselines, especially on triplets including multi-word entities and complex corresponding relationships.

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Availability of supporting data and code

The datasets and code are available from the corresponding author on reasonable request.

Notes

  1. https://stanfordnlp.github.io/CoreNLP/

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Authors and Affiliations

  1. School of Computer Science and Engineering, Shenyang Jianzhu University, Wusan Street, Shenyang, 110000, Liaoning, China

    Yandan Wang, Zhongtang Chen & Shuang Chen

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  1. Yandan Wang
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  2. Zhongtang Chen
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  3. Shuang Chen
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Contributions

All authors contributed to the study conception and design. Shuang Chen and Zhongtang Chen prepared material preparation, collected and analyzed data. Yandan Wang prepared figures and wrote the first draft of the manuscript, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yandan Wang.

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Wang, Y., Chen, Z. & Chen, S. ES-ASTE: enhanced span-level framework for aspect sentiment triplet extraction. J Intell Inf Syst 60, 593–612 (2023). https://doi.org/10.1007/s10844-023-00783-3

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