Search Results for author: Bevan Koopman

Found 7 papers, 3 papers with code

How does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval?

no code implementations12 May 2022 Hang Li, Ahmed Mourad, Bevan Koopman, Guido Zuccon

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.

Passage Retrieval

From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search

1 code implementation6 Apr 2022 Shuai Wang, Harrisen Scells, Justin Clark, Bevan Koopman, Guido Zuccon

However, we show pseudo seed studies are not representative of real seed studies used by information specialists.

Improving Chest X-Ray Report Generation by Leveraging Warm-Starting

1 code implementation24 Jan 2022 Aaron Nicolson, Jason Dowling, Bevan Koopman

Our experimental investigation demonstrates that the Convolutional vision Transformer (CvT) ImageNet-21K and the Distilled Generative Pre-trained Transformer 2 (DistilGPT2) checkpoints are best for warm-starting the encoder and decoder, respectively.

Natural Language Processing Text Generation

Semantic Search for Large Scale Clinical Ontologies

no code implementations1 Jan 2022 Duy-Hoa Ngo, Madonna Kemp, Donna Truran, Bevan Koopman, Alejandro Metke-Jimenez

Finding concepts in large clinical ontologies can be challenging when queries use different vocabularies.

Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls

1 code implementation25 Aug 2021 Hang Li, Ahmed Mourad, Shengyao Zhuang, Bevan Koopman, Guido Zuccon

Text-based PRF results show that the use of PRF had a mixed effect on deep rerankers across different datasets.

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