A context-dependent relevance model

Edward Kai Fung Dang, Wing Pong Robert Luk, James Allan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

� 2015 ASIS & T Numerous past studies have demonstrated the effectiveness of the relevance model (RM) for information retrieval (IR). This approach enables relevance or pseudo-relevance feedback to be incorporated within the language modeling framework of IR. In the traditional RM, the feedback information is used to improve the estimate of the query language model. In this article, we introduce an extension of RM in the setting of relevance feedback. Our method provides an additional way to incorporate feedback via the improvement of the document language models. Specifically, we make use of the context information of known relevant and nonrelevant documents to obtain weighted counts of query terms for estimating the document language models. The context information is based on the words (unigrams or bigrams) appearing within a text window centered on query terms. Experiments on several Text REtrieval Conference (TREC) collections show that our context-dependent relevance model can improve retrieval performance over the baseline RM. Together with previous studies within the BM25 framework, our current study demonstrates that the effectiveness of our method for using context information in IR is quite general and not limited to any specific retrieval model.
Original languageEnglish
Pages (from-to)582-593
Number of pages12
JournalJournal of the Association for Information Science and Technology
Volume67
Issue number3
DOIs
Publication statusPublished - 1 Mar 2016

Keywords

  • information retrieval

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences

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