Score distributions for Pseudo Relevance Feedback
نویسندگان
چکیده
منابع مشابه
Score distributions for Pseudo Relevance Feedback
Relevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the Pseudo Relevance Feedback task. It is known that one of the factors that more affect to the Pseudo Relevance Fee...
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Pseudo-relevance feedback (PRF) is an effective technique for improving the retrieval performance through updating the query model using the top retrieved documents. Previous work shows that estimating the effectiveness of feedback documents can substantially affect the PRF performance. Following the recent studies on theoretical analysis of PRF models, in this paper, we introduce a new constra...
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Successfully retrieving a web document is a twofold problem: having an adequate query that can usefully and properly help filtering relevant documents from huge collections, and presenting the user those that may indeed fulfill his/her needs. In this paper, we focus on the first issue – the problem of having a misleading user query. The aim of the work is to refine a query by using extracts ins...
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We discuss the idea of modelling the statistical distributions of scores of documents, classified as relevant or non-relevant. Various specific combinations of standard statistical distributions have been used for this purpose. Some theoretical considerations indicate problems with some of the choices of pairs of distributions. Specifically, we revisit a generalisation of the well-known inverse...
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ژورنال
عنوان ژورنال: Information Sciences
سال: 2014
ISSN: 0020-0255
DOI: 10.1016/j.ins.2014.03.034