{"type":"ScholarlyArticle","title":"Aspect Sentiment Classification with both Word-level and Clause-level Attention Networks","authors":[{"name":"Jingjing Wang","slug":"jingjing-wang","given":"Jingjing","family":"Wang","affiliation":"School of Computer Science and Technology , Soochow University , China"},{"name":"Jie Li","slug":"jie-li","given":"Jie","family":"Li","affiliation":"School of Computer Science and Engineering , Southeast University , China"},{"name":"Shoushan Li","slug":"shoushan-li","given":"Shoushan","family":"Li","affiliation":"School of Computer Science and Technology , Soochow University , China"},{"name":"Yangyang Kang","slug":"yangyang-kang","given":"Yangyang","family":"Kang","affiliation":"Alibaba Group , China"},{"name":"Min Zhang","slug":"min-zhang","given":"Min","family":"Zhang","affiliation":"School of Computer Science and Technology , Soochow University , China"},{"name":"Luo Si","slug":"luo-si","given":"Luo","family":"Si","affiliation":"Alibaba Group , China"},{"name":"Guodong Zhou","slug":"guodong-zhou","given":"Guodong","family":"Zhou","affiliation":"School of Computer Science and Technology , Soochow University , China"}],"abstract":"Aspect sentiment classification, a challenging task in sentiment analysis, has been attracting more and more attention in recent years. In this paper, we highlight the need for incorporating the importance degrees of both words and clauses inside a sentence and propose a hierarchical network with both word-level and clause-level attentions to aspect sentiment classification. Specifically, we first adopt sentence-level discourse segmentation to segment a sentence into several clauses. Then, we leverage multiple Bi-directional LSTM layers to encode all clauses and propose a word-level attention layer to capture the importance degrees of words in each clause. Third and finally, we leverage another Bi-directional LSTM layer to encode the outputs from the former layers and propose a clause-level attention layer to capture the importance degrees of all the clauses inside a sentence. Experimental results on the laptop and restaurant datasets from SemEval-2015 demonstrate the effectiveness of our proposed approach to aspect sentiment classification.","doi":"10.24963/ijcai.2018/617","url":"https://ijcai.org/proceedings/2018/617","pdf":"https://ijcai.org/proceedings/2018/0617.pdf","markdown":"https://ijcai.org/proceedings/2018/617.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/617","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":617,"pages":{"first":4439,"last":4445},"keywords":["Natural Language Processing: Sentiment Analysis and Text Mining","Natural Language Processing: NLP Applications and Tools"],"videos":[],"references":[{"position":1,"raw":"[Carlson et al., 2001] Lynn Carlson, Daniel Marcu, and Mary Ellen Okurovsky. 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