Abstract：
:The aim of this study is to explore the word sense disambiguation (WSD) problem across two biomedical domains-biomedical literature and clinical notes. A supervised machine learning technique was used for the WSD task. One of the challenges addressed is the creation of a suitable clinical corpus with manual sense annotations. This corpus in conjunction with the WSD set from the National Library of Medicine provided the basis for the evaluation of our method across multiple domains and for the comparison of our results to published ones. Noteworthy is that only 20% of the most relevant ambiguous terms within a domain overlap between the two domains, having more senses associated with them in the clinical space than in the biomedical literature space. Experimentation with 28 different feature sets rendered a system achieving an average F-score of 0.82 on the clinical data and 0.86 on the biomedical literature.
journal_name
J Biomed Informjournal_title
Journal of biomedical informaticsauthors
Savova GK,Coden AR,Sominsky IL,Johnson R,Ogren PV,de Groen PC,Chute CGdoi
10.1016/j.jbi.2008.02.003subject
Has Abstractpub_date
2008-12-01 00:00:00pages
1088-100issue
6eissn
1532-0464issn
1532-0480pii
S1532-0464(08)00024-5journal_volume
41pub_type
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