Research article

MBA: a literature mining system for extracting biomedical abbreviations

Yun Xu^1,2 , ZhiHao Wang^1,2 , YiMing Lei^1,2 , YuZhong Zhao^1,2 and Yu Xue³

¹Department of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui 230027, PR China

²Anhui Province-MOST Co-Key Laboratory of High Performance Computing and Its Application Hefei, Anhui 230027, PR China

³School of Life Science, University of Science and Technology of China Hefei, Anhui 230027, PR China

author email corresponding author email

BMC Bioinformatics 2009, 10:14doi:10.1186/1471-2105-10-14

Published:	9 January 2009

Abstract

Background

The exploding growth of the biomedical literature presents many challenges for biological researchers. One such challenge is from the use of a great deal of abbreviations. Extracting abbreviations and their definitions accurately is very helpful to biologists and also facilitates biomedical text analysis. Existing approaches fall into four broad categories: rule based, machine learning based, text alignment based and statistically based. State of the art methods either focus exclusively on acronym-type abbreviations, or could not recognize rare abbreviations. We propose a systematic method to extract abbreviations effectively. At first a scoring method is used to classify the abbreviations into acronym-type and non-acronym-type abbreviations, and then their corresponding definitions are identified by two different methods: text alignment algorithm for the former, statistical method for the latter.

Results

A literature mining system MBA was constructed to extract both acronym-type and non-acronym-type abbreviations. An abbreviation-tagged literature corpus, called Medstract gold standard corpus, was used to evaluate the system. MBA achieved a recall of 88% at the precision of 91% on the Medstract gold-standard EVALUATION Corpus.

Conclusion

We present a new literature mining system MBA for extracting biomedical abbreviations. Our evaluation demonstrates that the MBA system performs better than the others. It can identify the definition of not only acronym-type abbreviations including a little irregular acronym-type abbreviations (e.g., <CNS1, cyclophilin seven suppressor>), but also non-acronym-type abbreviations (e.g., <Fas, CD95>).