Proceedings

Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extraction with Rich Syntactic Knowledge

Shengqiong Wu, Hao Fei, Yafeng Ren, Donghong Ji, Jingye Li

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3957-3963.

https://doi.org/10.24963/ijcai.2021/545

Keywords

  • Natural Language Processing: Natural Language Semantics
  • Natural Language Processing: Information Extraction
  • Natural Language Processing: Sentiment Analysis and Text Mining

Abstract

In this paper, we propose to enhance the pair-wise aspect and opinion terms extraction (PAOTE) task by incorporating rich syntactic knowledge. We first build a syntax fusion encoder for encoding syntactic features, including a label-aware graph convolutional network (LAGCN) for modeling the dependency edges and labels, as well as the POS tags unifiedly, and a local-attention module encoding POS tags for better term boundary detection. During pairing, we then adopt Biaffine and Triaffine scoring for high-order aspect-opinion term pairing, in the meantime re-harnessing the syntax-enriched representations in LAGCN for syntactic-aware scoring. Experimental results on four benchmark datasets demonstrate that our model outperforms current state-of-the-art baselines, meanwhile yielding explainable predictions with syntactic knowledge.

Shengqiong Wu, Hao Fei, Yafeng Ren, Donghong Ji, and Jingye Li. 2021. Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extraction with Rich Syntactic Knowledge. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3957–3963. https://doi.org/10.24963/ijcai.2021/545
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