How Does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022 Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation for a second round of retrieval.
Hang Li +3 more
semanticscholar +1 more source
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
doaj +1 more source
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
doaj +1 more source
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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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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LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022 Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressions between a query and its potential relevant documents.
Yunchang Zhu +4 more
semanticscholar +1 more source
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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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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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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IITD-DBAI: Multi-Stage Retrieval with Pseudo-Relevance Feedback and Query Reformulation [PDF]
Text Retrieval Conference, 2022 Resolving the contextual dependency is one of the most challenging tasks in the Conversational system. Our submission to CAsT-2021 aimed to preserve the key terms and the context in all subsequent turns and use classical Information retrieval methods. It
Shivani Choudhary
semanticscholar +1 more source