Abstract
BackgroundThe 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.ResultsA 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.ConclusionWe 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>).
Highlights
The exploding growth of the biomedical literature presents many challenges for biological researchers
It is very challenging for biologists to keep up to date with their own field of biomedical research with biomedical knowledge expanding so quickly
Existing approaches fall into four broad categories: rule based, machine learning based, text alignment based, and statistically based
Summary
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 facilitates biomedical text analysis. An automatic method for biomedical knowledge text mining is urgently needed [1,2]. One special issue is the exploding use of new abbreviations [3]. It would be a great help for literature retrieval to collect these abbreviations automatically. Other text mining tasks could be done more efficiently if all the abbreviations for an entity could (page number not for citation purposes)
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