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Query Expansion Using Path-Constrained Random Walks

Bibliographic Details
Title: Query Expansion Using Path-Constrained Random Walks
Authors: Jianfeng Gao, Gu Xu, Jinxi Xu
Contributors: The Pennsylvania State University CiteSeerX Archives
Source: http://research.microsoft.com/en-us/um/people/jfgao/paper/2013/gao-xu-xu.sigir2013.camera.pdf.
Collection: CiteSeerX
Subject Terms: Algorithms, Experimentation Keywords Search Log, Query Expansion, Random Walk, Path Ranking Algorithm, Web Search
Description: This paper exploits Web search logs for query expansion (QE) by presenting a new QE method based on path-constrained random walks (PCRW), where the search logs are represented as a labeled, directed graph, and the probability of picking an expansion term for an input query is computed by a learned combination of constrained random walks on the graph. The method is shown to be generic in that it covers most of the popular QE models as special cases and flexible in that it provides a principled mathematical framework in which a wide variety of information useful for QE can be incorporated in a unified way. Evaluation is performed on the Web document ranking task using a real-world data set. Results show that the PCRW-based method is very effective for the expansion of rare queries, i.e., low-frequency queries that are unseen in search logs, and that it outperforms significantly other state-of-the-art QE methods.
Document Type: Thesis
File Description: application/pdf
Language: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.361.3141
Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.361.3141
http://research.microsoft.com/en-us/um/people/jfgao/paper/2013/gao-xu-xu.sigir2013.camera.pdf
Rights: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Accession Number: edsbas.895B39D1
Database: BASE
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  Data: Query Expansion Using Path-Constrained Random Walks
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  Data: <searchLink fieldCode="AR" term="%22Jianfeng+Gao%22">Jianfeng Gao</searchLink><br /><searchLink fieldCode="AR" term="%22Gu+Xu%22">Gu Xu</searchLink><br /><searchLink fieldCode="AR" term="%22Jinxi+Xu%22">Jinxi Xu</searchLink>
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  Data: The Pennsylvania State University CiteSeerX Archives
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  Data: <i>http://research.microsoft.com/en-us/um/people/jfgao/paper/2013/gao-xu-xu.sigir2013.camera.pdf</i>.
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  Data: This paper exploits Web search logs for query expansion (QE) by presenting a new QE method based on path-constrained random walks (PCRW), where the search logs are represented as a labeled, directed graph, and the probability of picking an expansion term for an input query is computed by a learned combination of constrained random walks on the graph. The method is shown to be generic in that it covers most of the popular QE models as special cases and flexible in that it provides a principled mathematical framework in which a wide variety of information useful for QE can be incorporated in a unified way. Evaluation is performed on the Web document ranking task using a real-world data set. Results show that the PCRW-based method is very effective for the expansion of rare queries, i.e., low-frequency queries that are unseen in search logs, and that it outperforms significantly other state-of-the-art QE methods.
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