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Block-based pseudo-relevance feedback for image retrieval
Journal of Experimental & Theoretical Artificial Intelligence, 2021Pseudo-relevance feedback (PRF) is a relevance feedback (RF) technique for information retrieval that treats the top k retrieved images as relevance feedback.
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Evaluation of Pseudo-Relevance Feedback using Wikipedia
Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval, 2019Users have specific information needs which are expressed in short queries to information retrieval systems. The queries are unstructured, and they tend to be short and ambiguous in most cases. Using the shallow language statistics including probabilistic or language models such as BM25 or Indri respectively can enhance the retrieval system metrics ...
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Pseudo relevance feedback using semantic clustering in relevance language model
Proceedings of the 18th ACM conference on Information and knowledge management, 2009Pseudo relevance feedback has demonstrated to be in general an effective technique for improving retrieval effectiveness, but the noise in the top retrieved documents still can cause topic drift problem that affects the performance of certain topics.
Qiang Pu, Daqing He
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Effective pseudo-relevance feedback for spoken document retrieval
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013With the exponential proliferation of multimedia associated with spoken documents, research on spoken document retrieval (SDR) has emerged and attracted much attention in the past two decades. Apart from much effort devoted to developing robust indexing and modeling techniques for representing spoken documents, a recent line of thought targets at the ...
Yi-Wen Chen +3 more
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Pseudo-Relevance Feedback Based on mRMR Criteria
2010Pseudo-relevance feedback has shown to be an effective method in many information retrieval tasks. Various criteria have been proposed to rank terms extracted from the top ranked document of the initial retrieval results. However, most existing methods extract terms individually and do not consider the impacts of relationships among terms and their ...
Yuanbin Wu +3 more
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Social Book Search with Pseudo-Relevance Feedback
2014Massive books with social information, e.g. reviews, rates and tags, have emerged in large numbers on the web. However, there are several limitations in traditional search methods for social books, as social books include complicated and various social information.
Bin Geng +5 more
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Pseudo-Relevance Feedback Driven for XML Query Expansion
Journal of Convergence Information Technology, 2010Pseudo-relevance feedback has been perceived as an effective solution for automatic query expansion. However, a recent study has shown that traditional pseudo-relevance feedback may bring into topic drift and hence be harmful to the retrieval performance.
Zhong Minjuan -, Wan Changxuan -
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Temporal Pseudo-relevance Feedback in Microblog Retrieval
2012Twitter has become a major outlet for news, discussion and commentary of on-going events and trends. Effective searching of Twitter collections poses a number of issues for traditional document-based information retrieval (IR) approaches, such as limited document term statistics and spam.
Stewart Whiting +2 more
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A Theoretical Analysis of Pseudo-Relevance Feedback Models
Proceedings of the 2013 Conference on the Theory of Information Retrieval, 2013Our goal in this study is to compare several widely used pseudo-relevance feedback (PRF) models and understand what explains their respective behavior. To do so, we first analyze how different PRF models behave through the characteristics of the terms they select and through their performance on two widely used test collections.
Stéphane Clinchant, Eric Gaussier
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An incremental approach to efficient pseudo-relevance feedback
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, 2013Pseudo-relevance feedback is an important strategy to improve search accuracy. It is often implemented as a two-round retrieval process: the first round is to retrieve an initial set of documents relevant to an original query, and the second round is to retrieve final retrieval results using the original query expanded with terms selected from the ...
Hao Wu, Hui Fang
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