Learn from Syntax: Improving Pair-Wise Aspect and Opinion Terms Extraction with Rich Syntactic Knowledge
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.
Cite
Text
Wu et al. "Learn from Syntax: Improving Pair-Wise Aspect and Opinion Terms Extraction with Rich Syntactic Knowledge." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/545Markdown
[Wu et al. "Learn from Syntax: Improving Pair-Wise Aspect and Opinion Terms Extraction with Rich Syntactic Knowledge." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/wu2021ijcai-learn/) doi:10.24963/IJCAI.2021/545BibTeX
@inproceedings{wu2021ijcai-learn,
title = {{Learn from Syntax: Improving Pair-Wise Aspect and Opinion Terms Extraction with Rich Syntactic Knowledge}},
author = {Wu, Shengqiong and Fei, Hao and Ren, Yafeng and Ji, Donghong and Li, Jingye},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {3957-3963},
doi = {10.24963/IJCAI.2021/545},
url = {https://mlanthology.org/ijcai/2021/wu2021ijcai-learn/}
}