A hybrid abbreviation extraction technique for biomedical literature

Document Type

Conference Proceeding

Publication Date

12-1-2007

Abstract

In this paper, we propose a novel technique to extract abbreviation combining natural language processing techniques and the Support Vector Machine (SVM) in biomedical literature. The proposed technique gives us the comparative advantages over others in the following aspects: 1) It incorporates lexical analysis techniques to supervised learning for extracting abbreviations. 2) It makes use of text chunking techniques to identify long forms of abbreviations. 3) It significantly improves Recall compared to other techniques. The experimental results show that our approach outperforms the leading abbreviation algorithms, ExtractAbbrev, ALICE, and Acrophile, at least by 6%, 13.9%, and 13.2% respectively, in both Precision and Recall on the Gold Standard Development corpus. © 2007 IEEE.

Identifier

49049098267 (Scopus)

ISBN

[0769530311, 9780769530314]

Publication Title

Proceedings 2007 IEEE International Conference on Bioinformatics and Biomedicine Bibm 2007

External Full Text Location

https://doi.org/10.1109/BIBM.2007.33

First Page

42

Last Page

47

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