A hybrid abbreviation extraction technique for biomedical literature

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.