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Term feedback for information retrieval with language models

Published: 23 July 2007 Publication History

Abstract

In this paper we study term-based feedback for information retrieval in the language modeling approach. With term feedback a user directly judges the relevance of individual terms without interaction with feedback documents, taking full control of the query expansion process. We propose a cluster-based method for selecting terms to present to the user for judgment, as well as effective algorithms for constructing refined query language models from user term feedback. Our algorithms are shown to bring significant improvement in retrieval accuracy over a non-feedback baseline, and achieve comparable performance to relevance feedback. They are helpful even when there are no relevant documents in the top.

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    cover image ACM Conferences
    SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
    July 2007
    946 pages
    ISBN:9781595935977
    DOI:10.1145/1277741
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 23 July 2007

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    Author Tags

    1. interactive retrieval
    2. query expansion

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    SIGIR07: The 30th Annual International SIGIR Conference
    July 23 - 27, 2007
    Amsterdam, The Netherlands

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    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    • (2022)Competitive SearchProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532771(2838-2849)Online publication date: 6-Jul-2022
    • (2018)Clustering small-sized collections of short textsInformation Retrieval10.1007/s10791-017-9324-821:4(273-306)Online publication date: 1-Aug-2018
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