Word Sense Disambiguation is the task of automatically identifying the appropriate sense (or concept) of an ambiguous word, for example, the term cold could refer to the temperature or a virus depending on the context in which it is used. Not being able to identify the intended concept of an ambiguous word negatively impacts the accuracy of biomedical applications such as medical coding and indexing which are becoming essential in the biomedical and clinical world with the push towards electronic medical records and the growing amount of information that is available to biomedical researchers and clinicians. This dissertation focuses on disambiguating ambiguous words in biomedical text.

This dissertation presents two methods, K-CUI and A-CUI, that can disambiguate ambiguous terms in any biomedical text using information from the Unified Medical Language System (UMLS). K-CUI explores the use of Concept Unique Identifiers (CUIs) as assigned by MetaMap, as features for a supervised learning method for word sense disambiguation. It also investigates four techniques to reduce the noise in the feature set by restricting which CUIs to include. The first technique is windowing, whose results show that in biomedical text indicative CUIs are highly localized. The second is a frequency cutoff, whose results show that when a dataset contains a high majority concept, the features that only occur a few times are essential in disambiguating the minority concepts. The third is a MetaMap Indexing cutoff, whose results show that word concepts are correlated with the topical information describing an instance. The fourth is a semantic similarity cutoff, whose results show in biomedical text, indicative features have a high semantic similarity with at least one of the possible concepts of the ambiguous word.