MBA: a literature mining system for extracting biomedical abbreviations.
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.


CONCLUSIONS
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>).
Xu
Yun
Y
Department of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui, PR China. xuyun@ustc.edu.cn
Wang
ZhiHao
Z
Lei
YiMing
Y
Zhao
YuZhong
Y
Xue
Yu
Y
D000004:Abbreviations as Topic; D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D016206:Databases, Bibliographic; D016208:Databases, Factual; D016247:Information Storage and Retrieval; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D012984:Software