Background: Like text in other domains, biomedical documents contain a range of terms with more than one possible meaning. These ambiguities form a significant obstacle to the aut...
Mark Stevenson, Yikun Guo, Robert J. Gaizauskas, D...
The huge volumes of biomedical texts available online drives the increasing need for automated techniques to analyze and extract knowledge from these repositories of information. ...
Approximately 57 different types of clinical annotations construct a patient's medical record. The annotations include radiology reports, discharge summaries, and surgical an...
John Pestian, Lukasz Itert, Charlotte Anderson, Wl...
This paper introduces an unsupervised vector approach to disambiguate words in biomedical text that can be applied to all-word disambiguation. We explore using contextual informat...
Recently, there has been a growth in the amount of machine readable information pertaining to the biomedical field. With this growth comes a desire to be able to extract informati...