Results 11 to 20 of about 10,903 (240)
Score distributions for Pseudo Relevance Feedback [PDF]
Information Sciences, 2014 Abstract 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.
Javier Parapar +2 more
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Journal of Information Science Theory and Practice, 2021 Pseudo relevance feedback (PRF) is a powerful query expansion (QE) technique that prepares queries using the top k pseudo-relevant documents and choosing expansion elements.
Farhan Yasir Hadi +3 more
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Query expansion using the clustering of pseudo relevant documents with query sensitive similarity [PDF]
مجله مدل سازی در مهندسی, 2017 Query expansion as one of query adaptation approaches, improves retrieval effectiveness of information retrieval. Pseudo-relevance feedback (PRF) is a query expansion approach that supposes top-ranked documents are relevant to the query concept, and ...
Reza Khodaei +2 more
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Contrastive Refinement for Dense Retrieval Inference in the Open-Domain Question Answering Task
Future Internet, 2023 In recent years, dense retrieval has emerged as the primary method for open-domain question-answering (OpenQA). However, previous research often focused on the query side, neglecting the importance of the passage side.
Qiuhong Zhai +3 more
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An Improved Retrievability-Based Cluster-Resampling Approach for Pseudo Relevance Feedback
Computers, 2016 Cluster-based pseudo-relevance feedback (PRF) is an effective approach for searching relevant documents for relevance feedback. Standard approach constructs clusters for PRF only on the basis of high similarity between retrieved documents.
Shariq Bashir
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Online Distillation for Pseudo-Relevance Feedback
, 2023 Model distillation has emerged as a prominent technique to improve neural search models. To date, distillation taken an offline approach, wherein a new neural model is trained to predict relevance scores between arbitrary queries and documents. In this paper, we explore a departure from this offline distillation strategy by investigating whether a ...
MacAvaney, Sean, Wang, Xi
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Patent query reduction using pseudo relevance feedback [PDF]
Proceedings of the 20th ACM international conference on Information and knowledge management, 2011 Queries in patent prior art search are full patent applications and much longer than standard ad hoc search and web search topics. Standard information retrieval (IR) techniques are not entirely effective for patent prior art search because of ambiguous terms in these massive queries. Reducing patent queries by extracting key terms has been shown to be
Ganguly, Debasis +3 more
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A Study of Word Bigrams for Pseudo-relevance Feedback in Information Retrieval [PDF]
Journal of Universal Computer ScienceTraditional information retrieval models mostly adopt a term independence assumption and are based on single terms or unigrams. Past efforts have attempted to go beyond this assumption, such as by using contiguous terms (i.e.
Edward Kai Fung Dang +2 more
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Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval [PDF]
Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval, 2021 10 ...
Wang, Xiao +3 more
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Improving Pseudo-Relevance Feedback With Neural Network-Based Word Representations
IEEE Access, 2018 In information retrieval, query expansion methods, such as pseudo-relevance feedback, are designed to enrich users' queries with relevant terms for comprehensively interpreting the desired information.
Bo Xu +4 more
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