Results 1 to 10 of about 11,682 (203)

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 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

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
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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

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, Jiawei Hou, Chenxu Zhu, Weinan Zhang, Ruiming Tang, Jincai Lai, Jieming Zhu, Xiuqiang He, Yong Yu   +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, Xiaoyu Zhang, Qiuhong Zhai, Chenyun Liu   +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

Online Distillation for Pseudo-Relevance Feedback

CoRR, 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 ...
Sean MacAvaney, Xi Wang 0012
openaire   +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.
openaire   +2 more sources

Word Embeddings-Based Pseudo Relevance Feedback Using Deep Averaging Networks for Arabic Document Retrieval

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, Noah Shahrul Azman Mohd, Mohd Masnizah, Atwan Jaffar   +3 more
doaj   +1 more source