fully utilize feedbacks: language model based relevance feedback in information retrieval

ISCAS OpenIR

	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
会议名称	7th International Conference on Advanced Data Mining and Applications, ADMA 2011
会议录名称	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
页码	395-405
会议日期	December 1
会议地点	Beijing, China
收录类别	EI
ISSN	0302-9743
ISBN	9783642258527
部门归属	(1) Key Laboratory of Machine Perception (Ministry of Education) School of Electronics Engineering and Computer Science Peking University Beijing 100871 China; (2) State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China
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. © 2011 Springer-Verlag.; 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. © 2011 Springer-Verlag.
关键词	Algorithms Computational Linguistics Data Mining Information Retrieval Research Supervised Learning Vector Spaces
主办者	IBM Research; China Samsung Telecom R and D Center; Tsinghua University
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16265
专题	中国科学院软件研究所
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[C],2011:395-405.