Exploring term dependences in probabilistic information retrieval model
Most previous information retrieval (IR) models assume that terms of queries and documents are statistically independent from each another. However, this kind of conditional independence assumption is obviously and openly understood to be wrong, so we present a new method of incorporating term depen...
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| Veröffentlicht in: | Information processing & management 2003-07, Vol.39 (4), p.505-519 |
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| container_title | Information processing & management |
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| creator | Cho, Bong-Hyun Lee, Changki Lee, Gary Geunbae |
| description | Most previous information retrieval (IR) models assume that terms of queries and documents are statistically independent from each another. However, this kind of conditional independence assumption is obviously and openly understood to be wrong, so we present a new method of incorporating term dependence in probabilistic retrieval model by adapting Bahadur–Lazarsfeld expansion (BLE) to compensate the weakness of the assumption. In this paper, we describe a theoretic process to apply BLE to the general probabilistic models and the state-of-the-art 2-Poisson model. Through the experiments on two standard document collections, HANTEC2.0 in Korean and WT10g in English, we demonstrate that incorporation of term dependences using the BLE significantly contribute to the improvement of performance in at least two different language IR systems. |
| doi_str_mv | 10.1016/S0306-4573(02)00078-X |
| format | Article |
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Documentation</topic><topic>Mathematical models</topic><topic>Online Searching</topic><topic>Poisson Probability Distribution</topic><topic>Probabilistic model</topic><topic>Probabilistic Models</topic><topic>Probabilistic retrieval</topic><topic>Probability</topic><topic>Relevance (Information Retrieval)</topic><topic>Sciences and techniques of general use</topic><topic>Searching</topic><topic>Studies</topic><topic>Subject Index Terms</topic><topic>Term dependence</topic><topic>Terms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cho, Bong-Hyun</creatorcontrib><creatorcontrib>Lee, Changki</creatorcontrib><creatorcontrib>Lee, Gary Geunbae</creatorcontrib><collection>ERIC</collection><collection>ERIC (Ovid)</collection><collection>ERIC</collection><collection>ERIC</collection><collection>ERIC (Legacy Platform)</collection><collection>ERIC( SilverPlatter )</collection><collection>ERIC</collection><collection>ERIC PlusText (Legacy Platform)</collection><collection>Education Resources Information Center (ERIC)</collection><collection>ERIC</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><jtitle>Information processing & management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cho, Bong-Hyun</au><au>Lee, Changki</au><au>Lee, Gary Geunbae</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><ericid>EJ670083</ericid><atitle>Exploring term dependences in probabilistic information retrieval model</atitle><jtitle>Information processing & management</jtitle><date>2003-07-01</date><risdate>2003</risdate><volume>39</volume><issue>4</issue><spage>505</spage><epage>519</epage><pages>505-519</pages><issn>0306-4573</issn><eissn>1873-5371</eissn><coden>IPMADK</coden><abstract>Most previous information retrieval (IR) models assume that terms of queries and documents are statistically independent from each another. 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| ispartof | Information processing & management, 2003-07, Vol.39 (4), p.505-519 |
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| subjects | 2-Poisson model Bahadur–Lazarsfeld expansion Comparative Analysis Computerized information storage and retrieval Exact sciences and technology Improvement Information and communication sciences Information Retrieval Information retrieval systems Information retrieval systems. Information and document management system Information science. Documentation Mathematical models Online Searching Poisson Probability Distribution Probabilistic model Probabilistic Models Probabilistic retrieval Probability Relevance (Information Retrieval) Sciences and techniques of general use Searching Studies Subject Index Terms Term dependence Terms |
| title | Exploring term dependences in probabilistic information retrieval model |
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