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| 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 | |
| Conference Name | 7th International Conference on Advanced Data Mining and Applications, ADMA 2011 |
| Source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 395-405 |
| Conference Date | December 1 |
| Conference Place | Beijing, China |
| 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 |
| 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.; 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. |
| Keyword | Algorithms Computational Linguistics Data Mining Information Retrieval Research Supervised Learning Vector Spaces |
| Sponsorship | IBM Research; China Samsung Telecom R and D Center; Tsinghua University |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16265 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation 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. |
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