Word sense disambiguation across two domains: biomedical literature and clinical notes.

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 Inform

authors

Savova GK,Coden AR,Sominsky IL,Johnson R,Ogren PV,de Groen PC,Chute CG

doi

10.1016/j.jbi.2008.02.003

subject

Has Abstract

pub_date

2008-12-01 00:00:00

pages

1088-100

issue

6

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(08)00024-5

journal_volume

41

pub_type

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