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	Unsupervised text pattern learning using minimum description length
	Wu, Ke ; Yu, Jiangsheng ; Wang, Hanpin ; Cheng, Fei
	2010
英文摘要	The knowledge of text patterns in a domain-specific corpus is valuable in many natural language processing (NLP) applications such as information extraction, question-answering system, and etc. In this paper, we propose a simple but effective probabilistic language model for modeling the in-decomposability of text patterns. Under the minimum description length (MDL) principle, an efficient unsupervised learning algorithm is implemented and the experiment on an English critical writing corpus has shown promising coverage of patterns compared with human summary. ?2010 IEEE.; EI; 0
语种	英语
DOI标识	10.1109/IUCS.2010.5666227
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/295413]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Wu, Ke,Yu, Jiangsheng,Wang, Hanpin,et al. Unsupervised text pattern learning using minimum description length. 2010-01-01.

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