{"type":"ScholarlyArticle","title":"Interactive Attention Networks for Aspect-Level Sentiment Classification","authors":[{"name":"Dehong Ma","slug":"dehong-ma","given":"Dehong","family":"Ma","affiliation":"MOE Key Lab of Computational Linguistics , Peking University , Beijing , 100871 , China"},{"name":"Sujian Li","slug":"sujian-li","given":"Sujian","family":"Li","affiliation":"MOE Key Lab of Computational Linguistics , Peking University , Beijing , 100871 , China; Collaborative Innovation Center for Language Ability , Xuzhou , Jiangsu , 221009 , China"},{"name":"Xiaodong Zhang","slug":"xiaodong-zhang","given":"Xiaodong","family":"Zhang","affiliation":"MOE Key Lab of Computational Linguistics , Peking University , Beijing , 100871 , China"},{"name":"Houfeng Wang","slug":"houfeng-wang","given":"Houfeng","family":"Wang","affiliation":"MOE Key Lab of Computational Linguistics , Peking University , Beijing , 100871 , China; Collaborative Innovation Center for Language Ability , Xuzhou , Jiangsu , 221009 , China"}],"abstract":"Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling thier contexts via generating  target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the  interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.","doi":"10.24963/ijcai.2017/568","url":"https://ijcai.org/proceedings/2017/568","pdf":"https://ijcai.org/proceedings/2017/0568.pdf","markdown":"https://ijcai.org/proceedings/2017/568.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/568","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Natural Language Processing","track":"Main track","number":568,"pages":{"first":4068,"last":4074},"keywords":["Natural Language Processing: Natural Language Processing","Natural Language Processing: Sentiment Analysis and Text Mining"],"videos":[],"references":[{"position":1,"raw":"[Bengio et al., 2003] Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 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