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

Bridget T. Mcinnes, Ted Pedersen, Ying Liu, Serguei V. Pakhomov, Genevieve B. Melton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationCoNLL 2011 - Fifteenth Conference on Computational Natural Language Learning, Proceedings of the Conference
Pages145-153
Number of pages9
StatePublished - Dec 1 2011
Event15th Conference on Computational Natural Language Learning, CoNLL 2011 - Portland, OR, United States
Duration: Jun 23 2011Jun 24 2011

Publication series

NameCoNLL 2011 - Fifteenth Conference on Computational Natural Language Learning, Proceedings of the Conference

Other

Other15th Conference on Computational Natural Language Learning, CoNLL 2011
CountryUnited States
CityPortland, OR
Period6/23/116/24/11

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