Article

Relevant query feedback in statistical language modeling

Authors:

Ramesh Nallapati,

James AllanAuthors Info & Claims

CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management

Pages 560 - 563

https://doi.org/10.1145/956863.956973

Published: 03 November 2003 Publication History

Abstract

In traditional relevance feedback, researchers have explored relevant document feedback, wherein, the query representation is updated based on a set of relevant documents returned by the user. In this work, we investigate relevant query feedback, in which we update a document's representation based on a set of relevant queries. We propose four statistical models to incorporate relevant query feedback.To validate our models, we considered anchor text of incoming links to a given document as feedback queries and performed experiments on the home-page retrieval task of TREC 2001. Our results show that three of our four models outperform the query-likelihood baseline by at least 35% in MRR score on a test set.

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Cited By

Yi XAllan J(2011)Discovering missing click-through query language information for web searchProceedings of the 20th ACM international conference on Information and knowledge management10.1145/2063576.2063604(153-162)Online publication date: 24-Oct-2011
https://dl.acm.org/doi/10.1145/2063576.2063604
Yi XAllan JCrestani FMarchand-Maillet SChen HEfthimiadis ESavoy J(2010)A content based approach for discovering missing anchor text for web searchProceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval10.1145/1835449.1835521(427-434)Online publication date: 19-Jul-2010
https://dl.acm.org/doi/10.1145/1835449.1835521
Dang VCroft BDavison BSuel TCraswell NLiu B(2010)Query reformulation using anchor textProceedings of the third ACM international conference on Web search and data mining10.1145/1718487.1718493(41-50)Online publication date: 4-Feb-2010
https://dl.acm.org/doi/10.1145/1718487.1718493

Index Terms

Relevant query feedback in statistical language modeling
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

cover image ACM Conferences

CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management

November 2003

592 pages

ISBN:1581137230

DOI:10.1145/956863

General Chair:
Donald Kraft
Louisiana State University
,
Program Chairs:
Ophir Frieder
Illinois Institute of Technology
,
Joachim Hammer
University of Florida
,
Sajda Qureshi
University of Nebraska, Omaha
,
Len Seligman
The MITRE Corporation

Copyright © 2003 ACM.

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Publication History

Published: 03 November 2003

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CIKM03: 12th International Conference on Information and Knowledge Management

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Cited By

Yi XAllan J(2011)Discovering missing click-through query language information for web searchProceedings of the 20th ACM international conference on Information and knowledge management10.1145/2063576.2063604(153-162)Online publication date: 24-Oct-2011
https://dl.acm.org/doi/10.1145/2063576.2063604
Yi XAllan JCrestani FMarchand-Maillet SChen HEfthimiadis ESavoy J(2010)A content based approach for discovering missing anchor text for web searchProceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval10.1145/1835449.1835521(427-434)Online publication date: 19-Jul-2010
https://dl.acm.org/doi/10.1145/1835449.1835521
Dang VCroft BDavison BSuel TCraswell NLiu B(2010)Query reformulation using anchor textProceedings of the third ACM international conference on Web search and data mining10.1145/1718487.1718493(41-50)Online publication date: 4-Feb-2010
https://dl.acm.org/doi/10.1145/1718487.1718493

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