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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

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.
core   +5 more sources

A multi-dimensional semantic pseudo-relevance feedback framework for information retrieval [PDF]

Scientific Reports
Pre-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, Yu Liu, Jinguang Chen, Ellen Anne Huang, Jimmy X. Huang   +4 more
doaj   +2 more sources

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, Manuel A Presedo-Quindimil, Alvaro Barreiro   +2 more
exaly   +2 more sources

Improved Arabic query expansion using word embedding [PDF]

Scientific Reports
Word 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, Sattam Almatarneh, Kaznah Alshammari, Mutasem Al-Smadi   +3 more
doaj   +2 more sources

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
Debasis Ganguly, Johannes Leveling, Walid Magdy, Gareth J. F. Jones   +3 more
openaire   +3 more sources

On Improving Pseudo-Relevance Feedback Using Pseudo-Irrelevant Documents [PDF]

, 2010
Pseudo-Relevance Feedback (PRF) assumes that the top-ranking n documents of the initial retrieval are relevant and extracts expansion terms from them. In this work, we introduce the notion of pseudo-irrelevant documents, i.e. high-scoring documents outside of top n that are highly unlikely to be relevant.
RAMAN, K, UDUPA, R, BHATTACHARYA, P, BHOLE, A   +3 more
openaire   +2 more sources

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, Macdonald, Craig, Tonellotto, Nicola, Ounis, Iadh   +3 more
openaire   +4 more sources

Automated Skin Cancer Report Generation via a Knowledge-Distilled Vision-Language Model [PDF]

IEEE Access
Artificial 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, Boyu Zhang, Hengyi Tian, Aleksandar Vakanski, Min Xian   +4 more
doaj   +2 more sources

Query dependent pseudo-relevance feedback based on wikipedia [PDF]

Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 2009
Pseudo-relevance feedback (PRF) via query-expansion has been proven to be e®ective in many information retrieval (IR) tasks. In most existing work, the top-ranked documents from an initial search are assumed to be relevant and used for PRF. One problem with this approach is that one or more of the top retrieved documents may be non-relevant, which can ...
Xu, Yang, Jones, Gareth J.F., Wang, Bin
openaire   +3 more sources