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.
['Bridget T. McInnes', 'Ted Pedersen', 'Ying Liu', 'Serguei V. S. Pakhomov', 'Genevieve B. Melton']
Using Second-order Vectors in a Knowledge-based Method for Acronym Disambiguation