Using second-order vectors in a knowledge-based method for acronym disambiguation

Authors:
Bridget T. McInnes;Ted Pedersen;Ying Liu;Serguei V. Pakhomov;Genevieve B. Melton
Affiliations:
University of Minnesota, Minneapolis, MN;University of Minnesota, Duluth, MN;University of Minnesota, Minneapolis, MN;University of Minnesota, Minneapolis, MN;University of Minnesota, Minneapolis, MN
Venue:
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Year:
2011

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Abstract

In this paper, we introduce a knowledge-based method to disambiguate biomedical acronyms using second-order co-occurrence vectors. We create these vectors using information about a long-form obtained from the Unified Medical Language System and Medline. We evaluate this method on a dataset of 18 acronyms found in biomedical text. Our method achieves an overall accuracy of 89%. The results show that using second-order features provide a distinct representation of the long-form and potentially enhances automated disambiguation.