{"type":"ScholarlyArticle","title":"Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extraction with Rich Syntactic Knowledge","authors":[{"name":"Shengqiong Wu","slug":"shengqiong-wu","given":"Shengqiong","family":"Wu","affiliation":""},{"name":"Hao Fei","slug":"hao-fei","given":"Hao","family":"Fei","affiliation":""},{"name":"Yafeng Ren","slug":"yafeng-ren","given":"Yafeng","family":"Ren","affiliation":""},{"name":"Donghong Ji","slug":"donghong-ji","given":"Donghong","family":"Ji","affiliation":""},{"name":"Jingye Li","slug":"jingye-li","given":"Jingye","family":"Li","affiliation":""}],"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.","doi":"10.24963/ijcai.2021/545","url":"https://ijcai.org/proceedings/2021/545","pdf":"https://ijcai.org/proceedings/2021/0545.pdf","markdown":"https://ijcai.org/proceedings/2021/545.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/545","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Natural Language Processing","track":"Main Track","number":545,"pages":{"first":3957,"last":3963},"keywords":["Natural Language Processing: Natural Language Semantics","Natural Language Processing: Information Extraction","Natural Language Processing: Sentiment Analysis and Text Mining"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}