Identification, expansion, and disambiguation of acronyms in biomedical texts

With the ever growing amount of biomedical literature there is an increasing desire to use sophisticated language processing algorithms to mine these texts. In order to use these algorithms we must first deal with acronyms, abbreviations, and misspellings. In this paper we look at identifying, expanding, and disambiguating acronyms in biomedical texts. We break the task up into three modular steps: Identification, Expansion, and Disambiguation. For Identification we use a hybrid approach that is composed of a naive Bayesian classifier and a couple of handcrafted rules. We are able to achieve results of 99.96% accuracy with a small training set. We break the expansion up into two categories, local and global expansion. For local expansion we use windowing and longest common subsequence to generate the possible expansion. Global expansion requires an acronym database. To disambiguate the different candidate expansions we use WordNet and semantic similarity, Overall we obtain a recall and precision of over 91%