{"type":"ScholarlyArticle","title":"Aspect-based Sentiment Analysis with Opinion Tree Generation","authors":[{"name":"Xiaoyi Bao","slug":"xiaoyi-bao","given":"Xiaoyi","family":"Bao","affiliation":"Natural Language Processing Lab , Soochow University , Suzhou , China"},{"name":"Wang Zhongqing","slug":"wang-zhongqing","given":"Wang","family":"Zhongqing","affiliation":"Natural Language Processing Lab , Soochow University , Suzhou , China"},{"name":"Xiaotong Jiang","slug":"xiaotong-jiang","given":"Xiaotong","family":"Jiang","affiliation":"Natural Language Processing Lab , Soochow University , Suzhou , China"},{"name":"Rong Xiao","slug":"rong-xiao","given":"Rong","family":"Xiao","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Shoushan Li","slug":"shoushan-li","given":"Shoushan","family":"Li","affiliation":"Natural Language Processing Lab , Soochow University , Suzhou , China"}],"abstract":"Existing studies usually extract these sentiment elements by decomposing the complex structure prediction task into multiple subtasks. Despite their effectiveness, these methods ignore the semantic structure in ABSA problems and require extensive task-specific designs. In this study, we introduce an Opinion Tree Generation task, which aims to jointly detect all sentiment elements in a tree. We believe that the opinion tree can reveal a more comprehensive and complete aspect-level sentiment structure. Furthermore, we propose a pre-trained model to integrate both syntax and semantic features for opinion tree generation. On one hand, a pre-trained model with large-scale unlabeled data is important for the tree generation model. On the other hand, the syntax and semantic features are very effective for forming the opinion tree structure.  Extensive experiments show the superiority of our proposed method. The results also validate the tree structure is effective to generate sentimental elements.","doi":"10.24963/ijcai.2022/561","url":"https://ijcai.org/proceedings/2022/561","pdf":"https://ijcai.org/proceedings/2022/0561.pdf","markdown":"https://ijcai.org/proceedings/2022/561.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/561","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Natural Language Processing","track":"Main Track","number":561,"pages":{"first":4044,"last":4050},"keywords":["Natural Language Processing: Information Extraction","Natural Language Processing: Sentiment Analysis and Text Mining"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986524","length_sec":103,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984667","length_sec":363,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Banarescu et al., 2013] Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Herm- jakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 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