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Title:
fully utilize feedbacks: language model based relevance feedback in information retrieval
Author: Lv Sheng-Long ; Deng Zhi-Hong ; Yu Hang ; Gao Ning ; Jiang Jia-Jian
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference Name: 7th International Conference on Advanced Data Mining and Applications, ADMA 2011
Conference Date: December 1
Issued Date: 2011
Conference Place: Beijing, China
Keyword: Algorithms ; Computational linguistics ; Data mining ; Information retrieval ; Research ; Supervised learning ; Vector spaces
Indexed Type: EI
ISSN: 0302-9743
ISBN: 9783642258527
Department: (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
Sponsorship: IBM Research; China Samsung Telecom R and D Center; Tsinghua University
Abstract: 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.
English Abstract: 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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16265
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Lv Sheng-Long,Deng Zhi-Hong,Yu Hang,et al. fully utilize feedbacks: language model based relevance feedback in information retrieval[C]. 见:7th International Conference on Advanced Data Mining and Applications, ADMA 2011. Beijing, China. December 1.
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