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	Learning Word Sense Embeddings from Word Sense Definitions
	Li, Qi ; Li, Tianshi ; Chang, Baobao
	2016
关键词	Word sense embedding RNN WordNet
英文摘要	Word embeddings play a significant role in many modern NLP systems. Since learning one representation per word is problematic for polysemous words and homonymous words, researchers propose to use one embedding per word sense. Their approaches mainly train word sense embeddings on a corpus. In this paper, we propose to use word sense definitions to learn one embedding per word sense. Experimental results on word similarity tasks and a word sense disambiguation task show that word sense embeddings produced by our approach are of high quality.; National Key Basic Research Program of China [2014CB340504]; National Natural Science Foundation of China [61273318]; CPCI-S(ISTP); 224-235; 10102
语种	英语
出处	5th International Conference on Natural Language Processing and Chinese Computing (NLPCC) / 24th International Conference on Computer Processing of Oriental Languages (ICCPOL)
DOI标识	10.1007/978-3-319-50496-4_19
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/470112]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Li, Qi,Li, Tianshi,Chang, Baobao. Learning Word Sense Embeddings from Word Sense Definitions. 2016-01-01.

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