{"type":"ScholarlyArticle","title":"Lexical Sememe Prediction via Word Embeddings and Matrix Factorization","authors":[{"name":"Ruobing Xie","slug":"ruobing-xie","given":"Ruobing","family":"Xie","affiliation":"Department of Computer Science and Technology , State Key Lab on Intelligent Technology and Systems ,"},{"name":"Xingchi Yuan","slug":"xingchi-yuan","given":"Xingchi","family":"Yuan","affiliation":"Department of Computer Science and Technology , State Key Lab on Intelligent Technology and Systems ,"},{"name":"Zhiyuan Liu","slug":"zhiyuan-liu","given":"Zhiyuan","family":"Liu","affiliation":"Department of Computer Science and Technology , State Key Lab on Intelligent Technology and Systems ,; Jiangsu Collaborative Innovation Center for Language Ability , Jiangsu Normal University , China; National Lab for Information Science and Technology , Tsinghua University , China"},{"name":"Maosong Sun","slug":"maosong-sun","given":"Maosong","family":"Sun","affiliation":"Department of Computer Science and Technology , State Key Lab on Intelligent Technology and Systems ,; Jiangsu Collaborative Innovation Center for Language Ability , Jiangsu Normal University , China"}],"abstract":"Sememes are defined as the minimum semantic units of human languages. People have manually annotated lexical sememes for words and form linguistic knowledge bases. However, manual construction is time-consuming and labor-intensive, with significant annotation inconsistency and noise. In this paper, we for the first time explore to automatically predict lexical sememes based on semantic meanings of words encoded by word embeddings. Moreover, we apply matrix factorization to learn semantic relations between sememes and words. In experiments, we take a real-world sememe knowledge base HowNet for training and evaluation, and the results reveal the effectiveness of our method for lexical sememe prediction. Our method will be of great use for annotation verification of existing noisy sememe knowledge bases and annotation suggestion of new words and phrases.","doi":"10.24963/ijcai.2017/587","url":"https://ijcai.org/proceedings/2017/587","pdf":"https://ijcai.org/proceedings/2017/0587.pdf","markdown":"https://ijcai.org/proceedings/2017/587.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/587","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":587,"pages":{"first":4200,"last":4206},"keywords":["Natural Language Processing: Natural Language Semantics","Natural Language Processing: Natural Language Processing"],"videos":[],"references":[{"position":1,"raw":"[Auer et al., 2007] Sören Auer, Christian Bizer, Georgi Ko- bilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 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