QQ客服 官方微博 反馈留言

	Fully Utilize Feedbacks: Language Model Based Relevance Feedback in Information Retrieval
	Lv, Sheng-Long ; Deng, Zhi-Hong ; Yu, Hang ; Gao, Ning ; Jiang, Jia-Jian
	2011
关键词	Relevance Feedback Language model Semi-supervised Learning Irrelevant Document
英文摘要	Relevance feedback algorithm is proposed to be an effective way to improve the precision of information retrieval. However, most researches about relevance feedback are based on vector space model, which can't be used in other more complicated and powerful models, such as language model and logic model. Meanwhile, other researches are conceptually restricted to the view of a query as a set of terms, and so cannot be naturally applied to more general case when the query is considered as a sequence of terms and the frequency information of a query tern is considered. In this paper, we mainly focuses on relevant feedback Algorithm based on language model. We use a mixture model to describe the process of generating document and use EM to solve model's parameters. Our research also employs semi-supervised learning to calculate collection model and proposes an effective way to obtain feedback from irrelevant documents to improve our algorithm.; Computer Science, Artificial Intelligence; Computer Science, Information Systems; CPCI-S(ISTP); 0
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/292902]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Lv, Sheng-Long,Deng, Zhi-Hong,Yu, Hang,et al. Fully Utilize Feedbacks: Language Model Based Relevance Feedback in Information Retrieval. 2011-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。