Co-occurrence graphs for word sense disambiguation in the biomedical domain
- Submitting institution
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The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2498
- Type
- D - Journal article
- DOI
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10.1016/j.artmed.2018.03.002
- Title of journal
- Artificial Intelligence in Medicine
- Article number
- -
- First page
- 9
- Volume
- 87
- Issue
- -
- ISSN
- 0933-3657
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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D - Natural Language Processing
- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper was published in a top 3 medical informatics journal (JCR2019) and is the first to apply co-occurrence graphs to the long standing and widely explored problem of WSD in the biomedical domain; achieving state-of-the-art performance on two standard datasets. This work was undertaken in collaboration with researchers at UNED, Madrid, Spain and was selected for inclusion in the review of significant papers in the Yearbook of Medical Informatics, 2018 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697498/).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -