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Improving Span-Based Aspect Sentiment Triplet Extraction with Abundant Syntax Knowledge

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Abstract

Aspect sentiment triplet extraction is a subtask of aspect based sentiment analysis, which has attracted considerable attention in recent years. It aims to obtain the aspect term, the associated opinion term and sentiment polarity, so called the triplet in reviews. Most existing studies can only learn the word-level context information and capture the superficial interactions between aspect and opinion terms. In contrast, our proposed span-based aspect sentiment triplet extraction model incorporates abundant syntax information, namely the span-level part-of-speech, constituent syntax, and dependency syntax information. Therefore, our model can learn both the connections between multiple words within the span and the interactions between aspect and opinion terms, and thus improve the performance on triplet extraction. Extensive experimental results show that our model outperforms previous models on four benchmark datasets and achieves state-of-the-art performance.

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Notes

  1. BIOES tagging scheme includes “begin”, “inside”, “outside”, “end” and “single” labels.

  2. Our codes are available at: https://github.com/FengLingCong13/SBSK-ASTE.

  3. Available at: https://www.nltk.org/.

  4. Available at: https://stanfordnlp.github.io/CoreNLP/.

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Acknowledgements

We sincerely thank the editors and reviewers. This work is supported by the Guangdong basic and applied basic research fund (No. 2021A1515011171), the Guangdong General Colleges and Universities Special Projects in Key Areas of Artificial Intelligence of China (No. 2019KZDZX1033), and the Guangzhou basic research plan, basic and applied basic research project (No. 202102080282).

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Authors and Affiliations

  1. School of Software, South China Normal University, Foshan, 528225, Guangdong, China

    Lingcong Feng, Biqing Zeng, Lewei He, Pengfei Chen, Zipeng Huang & Weihua Du

  2. School of Computer Science, Wuhan University, Wuhan, 430079, Hubei, China

    Mayi Xu

  3. School of Computer Science, Guangdong AIB Polytechnic, Guangzhou, 510630, Guangdong, China

    Huimin Deng

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  1. Lingcong Feng
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Feng, L., Zeng, B., He, L. et al. Improving Span-Based Aspect Sentiment Triplet Extraction with Abundant Syntax Knowledge. Neural Process Lett 55, 5833–5854 (2023). https://doi.org/10.1007/s11063-022-11115-x

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