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Syntactic and semantic dual-enhanced bidirectional network for aspect sentiment triplet extraction

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Abstract

Span-level method achieves competitive results in Aspect Sentiment Triplet Extraction (ASTE) by enumerating all possible spans. However, previous span-level methods fail to exploit syntactic information to identify the correspondence between aspect terms and opinion terms, which makes the extracted triplets inaccurate. In this paper, we propose a syntactic and semantic dual-enhanced bidirectional network (SSBN) for ASTE task. By constructing word dependencies as a graph and embedding them into features to capture syntactic information more effectively in bidirectional network. Furthermore, we design a pruning strategy that uses part-of-speech information to alleviate the problem of identifying potential aspects and opinions from a large number of spans. We conduct extensive experiments on four benchmark datasets, and the experimental results demonstrate the effectiveness of the SSBN model.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://github.com/xuuuluuu/SemEval-Triplet-data/tree/master/ASTE-Data-V2-EMNLP2020.

  2. Code is publicly available at https://github.com/wang-liangzai/SSBN.git

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Acknowledgements

This work was supported in part Key R & D project of Shandong Province 2019JZZY010129, and in part by the Shandong Provincial Social Science Planning Project under Award 19BJCJ51, Award 18CXWJ01, and Award 18BJYJ04.

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  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China

    Guangjin Wang, Yuanying Wang, Fuyong Xu, Yongsheng Zhang & Peiyu Liu

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  1. Guangjin Wang
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Correspondence to Peiyu Liu.

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Wang, G., Wang, Y., Xu, F. et al. Syntactic and semantic dual-enhanced bidirectional network for aspect sentiment triplet extraction. J Supercomput 80, 3025–3041 (2024). https://doi.org/10.1007/s11227-023-05573-w

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