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http://hdl.handle.net/10397/106274
Title: | A context-dependent relevance model | Authors: | Dang, EKF Luk, RWP Allan, J |
Issue Date: | Mar-2016 | Source: | Journal of the Association for Information Science and Technology, Mar. 2016, v. 67, no. 3, p. 582-593 | Abstract: | 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. | Publisher: | John Wiley & Sons | Journal: | Journal of the Association for Information Science and Technology | ISSN: | 2330-1635 | EISSN: | 2330-1643 | DOI: | 10.1002/asi.23419 | Rights: | ©2015 ASIS&T This is the peer reviewed version of the following article: Dang, E.K.F., Luk, R.W.P. and Allan, J. (2016), A Context-Dependent Relevance Model. J Assn Inf Sci Tec, 67: 582-593, which has been published in final form at https://doi.org/10.1002/asi.23419. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. |
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