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	Improving Word Vector with Prior Knowledge in Semantic Dictionary
	Li, Wei ; Wu, Yunfang ; Lv, Xueqiang
	2016
关键词	Rare words Semantic dictionary Morphological information Word embedding
英文摘要	Using low dimensional vector space to represent words has been very effective in many NLP tasks. However, it doesn't work well when faced with the problem of rare and unseen words. In this paper, we propose to leverage the knowledge in semantic dictionary in combination with some morphological information to build an enhanced vector space. We get an improvement of 2.3% over the state-of-the-art Heidel Time system in temporal expression recognition, and obtain a large gain in other name entity recognition (NER) tasks. The semantic dictionary Hownet alone also shows promising results in computing lexical similarity.; National Natural Science Foundation of China [61371129]; National Key Basic Research Program of China [2014CB340504]; Key Program of Social Science foundation of China [12ZD227]; Opening Project of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research [ICDD201402]; CPCI-S(ISTP); 461-469; 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_38
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/470120]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Li, Wei,Wu, Yunfang,Lv, Xueqiang. Improving Word Vector with Prior Knowledge in Semantic Dictionary. 2016-01-01.

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