Results 1 to 10 of about 10,903 (240)
Improved biomedical term selection in pseudo relevance feedback. [PDF]
Database (Oxford), 2018 Biomedical information retrieval systems are becoming popular and complex due to massive amount of ever-growing biomedical literature. Users are unable to construct a precise and accurate query that represents the intended information in a clear manner.
Nabeel Asim M +3 more
europepmc +4 more sources
Evaluation of Term Ranking Algorithms for Pseudo-Relevance Feedback in MEDLINE Retrieval [PDF]
Healthcare Informatics Research, 2011 ObjectivesThe purpose of this study was to investigate the effects of query expansion algorithms for MEDLINE retrieval within a pseudo-relevance feedback framework.MethodsA number of query expansion algorithms were tested using various term ranking ...
Sooyoung Yoo, Jinwook Choi
doaj +2 more sources
A multi-dimensional semantic pseudo-relevance feedback framework for information retrieval [PDF]
Scientific ReportsPre-trained models have garnered significant attention in the field of information retrieval, particularly for improving document ranking. Typically, an initial retrieval step using sparse methods such as BM25 is employed to obtain a set of pseudo ...
Min Pan +4 more
doaj +2 more sources
Improved Arabic query expansion using word embedding [PDF]
Scientific ReportsWord embedding enhances pseudo-relevance feedback query expansion (PRFQE), but training word embedding models takes a long time and is applied to large datasets.
Yaser A. Al-Lahham +3 more
doaj +2 more sources
Automated Skin Cancer Report Generation via a Knowledge-Distilled Vision-Language Model [PDF]
IEEE AccessArtificial Intelligence (AI)’s capacity to analyze dermoscopic images promises a groundbreaking leap in skin cancer diagnostics, offering exceptional accuracy and an effortlessly non-invasive image acquisition process.
Lawhori Chakrabarti +4 more
doaj +2 more sources
QA4PRF: A Question Answering Based Framework for Pseudo Relevance Feedback
IEEE Access, 2021 Pseudo relevance feedback (PRF) automatically performs query expansion based on top-retrieved documents to better represent the user’s information need so as to improve the search results.
Handong Ma +8 more
doaj +1 more source
A Hybrid Text Generation-Based Query Expansion Method for Open-Domain Question Answering
Future Internet, 2023 In the two-stage open-domain question answering (OpenQA) systems, the retriever identifies a subset of relevant passages, which the reader then uses to extract or generate answers.
Wenhao Zhu +3 more
doaj +1 more source
Multitask Fine-Tuning for Passage Re-Ranking Using BM25 and Pseudo Relevance Feedback
IEEE Access, 2022 Passage re-ranking is a machine learning task that estimates relevance scores between a given query and candidate passages. Keyword features based on the lexical similarities between queries and passages have been traditionally used for the passage re ...
Meoungjun Kim, Youngjoong Ko
doaj +1 more source
Measuring pseudo relevance feedback & CLIR [PDF]
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, 2004 In this poster, we report on the effects of pseudo relevance feedback (PRF) for a cross language image retrieval task using a test collection. Typically PRF has been shown to improve retrieval performance in previous CLIR experiments based on average precision at a fixed rank.
Sanderson, M., Clough, P.
openaire +2 more sources
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
openaire +1 more source