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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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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A boosting approach to improving pseudo-relevance feedback [PDF]
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, 2011 Pseudo-relevance feedback has proven effective for improving the average retrieval performance. Unfortunately, many experiments have shown that although pseudo-relevance feedback helps many queries, it also often hurts many other queries, limiting its usefulness in real retrieval applications.
Yuanhua Lv, ChengXiang Zhai, Wan Chen
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Positional relevance model for pseudo-relevance feedback [PDF]
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, 2010 Pseudo-relevance feedback is an effective technique for improving retrieval results. Traditional feedback algorithms use a whole feedback document as a unit to extract words for query expansion, which is not optimal as a document may cover several different topics and thus contain much irrelevant information.
Yuanhua Lv, ChengXiang Zhai
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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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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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Aggregation of Multiple Pseudo Relevance Feedbacks for Image Search Re-Ranking
IEEE Access, 2019 Image retrieval effectiveness can be improved by pseudo relevance feedback (PRF), which automatically uses top-$k$ images of the initial retrieval result as the pseudo feedback.
Wei-Chao Lin
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Query Expansion for Arabic Information Retrieval Model: Performance Analysis and Modification [PDF]
The Egyptian Journal of Language Engineering, 2018 Information retrieval aims to find all relevant documents responding to a query from textual data. A goodinformation retrieval system should retrieve only those documents that satisfy the user query.
Ayat Elnahaas +4 more
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