fully utilize feedbacks: language model based relevance feedback in information retrieval
文献类型：会议论文
| 作者 | Lv Sheng-Long ; Deng Zhi-Hong ; Yu Hang ; Gao Ning ; Jiang Jia-Jian |
| 出版日期 | 2011 |
| 会议名称 | 7th International Conference on Advanced Data Mining and Applications, ADMA 2011 |
| 会议日期 | December 1 |
| 会议地点 | Beijing, China |
| 关键词 | Algorithms Computational linguistics Data mining Information retrieval Research Supervised learning Vector spaces |
| 页码 | 395-405 |
| 中文摘要 | 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. © 2011 Springer-Verlag. |
| 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. © 2011 Springer-Verlag. |
| 收录类别 | EI |
| 会议主办者 | IBM Research; China Samsung Telecom R and D Center; Tsinghua University |
| 会议录 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
 |
| 语种 | 英语 |
| ISSN号 | 0302-9743 |
| ISBN号 | 9783642258527 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16265]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 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]. 见:7th International Conference on Advanced Data Mining and Applications, ADMA 2011. Beijing, China. December 1. |
入库方式： OAI收割
来源：软件研究所
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