Title

Identification, Expansion, And Disambiguation Of Acronyms In Biomedical Texts

Keywords

Acronyms; Information Retrieval; Natural Language Processing; Text Cleansing

Abstract

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 expansions. 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%. © Springer-Verlag Berlin Heidelberg 2005.

Publication Date

12-1-2005

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

3759 LNCS

Number of Pages

186-195

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/11576259_21

Socpus ID

33646673384 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33646673384

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