{"type":"ScholarlyArticle","title":"Unsupervised Neural Aspect Extraction with Sememes","authors":[{"name":"Ling Luo","slug":"ling-luo","given":"Ling","family":"Luo","affiliation":"Key Lab of Intelligent Information Processing , Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences"},{"name":"Xiang Ao","slug":"xiang-ao","given":"Xiang","family":"Ao","affiliation":"Key Lab of Intelligent Information Processing , Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences"},{"name":"Yan Song","slug":"yan-song","given":"Yan","family":"Song","affiliation":"Tencent AI Lab"},{"name":"Jinyao Li","slug":"jinyao-li","given":"Jinyao","family":"Li","affiliation":"Institute of Software , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Xiaopeng Yang","slug":"xiaopeng-yang","given":"Xiaopeng","family":"Yang","affiliation":"David R. Cheriton School of Computer Science , Faculty of Mathematics , University of Waterloo"},{"name":"Qing He","slug":"qing-he","given":"Qing","family":"He","affiliation":"Key Lab of Intelligent Information Processing , Institute of Computing Technology , Chinese Academy of Sciences , Beijing , China; University of Chinese Academy of Sciences"},{"name":"Dong Yu","slug":"dong-yu","given":"Dong","family":"Yu","affiliation":"Tencent AI Lab"}],"abstract":"Aspect extraction relies on identifying aspects by discovering coherence among words, which is challenging when word meanings are diversified and processing on short texts. To enhance the performance on aspect extraction, leveraging lexical semantic resources is a possible solution to such challenge. In this paper, we present an unsupervised neural framework that leverages sememes to enhance lexical semantics. The overall framework is analogous to an autoenoder which reconstructs sentence representations and learns aspects by latent variables. Two models that form sentence representations are proposed by exploiting sememes via (1) a hierarchical attention; (2) a context-enhanced attention. Experiments on two real-world datasets demonstrate the validity and the effectiveness of our models, which significantly outperforms existing baselines.","doi":"10.24963/ijcai.2019/712","url":"https://ijcai.org/proceedings/2019/712","pdf":"https://ijcai.org/proceedings/2019/0712.pdf","markdown":"https://ijcai.org/proceedings/2019/712.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/712","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Natural Language Processing","track":"Main track","number":712,"pages":{"first":5123,"last":5129},"keywords":["Natural Language Processing: Information Extraction","Natural Language Processing: Sentiment Analysis and Text Mining","Natural Language Processing: Natural Language Semantics"],"videos":[],"references":[{"position":1,"raw":"Leonard Bloomfield. 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