MBA: a literature mining system for extracting biomedical abbreviations

BMC Bioinformatics. 2009 Jan 9;10:14. doi: 10.1186/1471-2105-10-14.


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>).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Abbreviations as Topic*
  • Abstracting and Indexing / methods*
  • Algorithms
  • Databases, Bibliographic
  • Databases, Factual
  • Information Storage and Retrieval
  • Natural Language Processing
  • Pattern Recognition, Automated / methods
  • Software*