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Identification, Expansion, and Disambiguation of Acronyms in Biomedical Texts

  • David B. Bracewell
  • Scott Russell
  • Annie S. Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3759)

Abstract

With the ever growing amount of biomedical literature there is an increasing desire to use sophisticated language processing algorithms to mine these texts. In order to use these algorithms we must first deal with acronyms, abbreviations, and misspellings.In this paper we look at identifying, expanding, and disambiguating acronyms in biomedical texts. We break the task up into three modular steps: Identification, Expansion, and Disambiguation. For Identification we use a hybrid approach that is composed of a naive Bayesian classifier and a couple of handcrafted rules. We are able to achieve results of 99.96% accuracy with a small training set. We break the expansion up into two categories, local and global expansion. For local expansion we use windowing and longest common subsequence to generate the possible expansions. Global expansion requires an acronym database. To disambiguate the different candidate expansions we use WordNet and semantic similarity. Overall we obtain a recall and precision of over 91%.

Keywords

Acronyms Text Cleansing Information Retrieval Natural Language Processing 

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References

  1. 1.
    Ao, H., Takagi, T.: An Algorithm to Identify Abbreviations from MEDLINE. Genome Informatics 14, 697–698 (2003)Google Scholar
  2. 2.
    Bigert, J., Knutsson, O., Sjobergh, J.: Automatic Evaluation of Robustness and Degradation in Tagging and Parsing. In: Proceedings of the 2003 International Conference on Recent Advances in Natural Language Processing, pp. 51–62 (2003)Google Scholar
  3. 3.
    Friedman, C., Liu, H., Shagina, L., Johnson, S., Hripcsack, G.: Evaluating the UMLS as a Source of Lexical Knowledge for Medical Language Processing. In: Proceedings of American Medical Informatics Association Symposium, pp. 189–193 (2001)Google Scholar
  4. 4.
    Gale, W., Church, K., Yarowsky, D.: One Sense Per Discourse. In: Proceedings of the DARPA Workshop on Speech and Natural Language Processing, pp. 233–237 (1992)Google Scholar
  5. 5.
    Jiang, J.J., Conrath, D.W.: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In: Proceedings of the Int’l Conf. on Research on Computational Linguistics (1997)Google Scholar
  6. 6.
    Liu, H., Aronson, A., Friedman, C.: A study of abbreviations in MEDLINE abstracts. In: Proceedings of American Medical Informatics Association Symposium, pp. 464–469 (2002)Google Scholar
  7. 7.
    Miller, G.: WordNet: a lexical database for English. Communications of the ACM 38, 39–41 (1995)CrossRefGoogle Scholar
  8. 8.
    Pakhomov, S.: Semi-supervised maximum entropy based approach to acronym and abbreviation normalization in medical texts. In: Proceediags of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 160–167 (2002)Google Scholar
  9. 9.
    Park, Y., Bryd, R.: Hybrid Text Mining for Finding Abbreviations and their Definitions. In: Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing, pp. 126–133 (2001)Google Scholar
  10. 10.
    Pustejovsky, J., Castano, J., Cochran, B., Kotecki, M., Morrell, M., Rumshisky, A.: Linguistic Knowledge Extraction from Medline: Automatic Construction of an Acronym Database. In: Proceedings of Medinfo. (2001)Google Scholar
  11. 11.
    Taghva, K., Gilbreth, J.: “Recognizing Acronyms and their Definitions”, Technical Report 95-03, ISRI (Information Science Research Institute) UNLV (1995)Google Scholar
  12. 12.
    Van Delden, S., Bracewell, D.B., Gomez, F.: Supervised and Unsupervised Automatic Spelling Correction. In: Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration (2004)Google Scholar
  13. 13.
    Yang, Y., Liu, X.: A Re-Examination of Text Categorization Methods. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 42–49 (1999)Google Scholar
  14. 14.
    Yu, Z., Tsuruoka, Y., Tsujii, J.: Automatic Resolution of Ambiguous Abbreviations in Biomedical Texts using Support Vector Machines and One Sense Per Discourse Hypothesis. In: Proceedings of the SIGIR 2003 Workshop on Text Analysis and Search for Bioinformatics, pp. 57–62 (2003)Google Scholar
  15. 15.
    Zahariev, M.: Efficient Acronym-Expansion Matching for Automatic Acronym Acquisition. In: Proceedings of the International Conference on Information and Knowledge Engineering, pp. 32–37 (2003)Google Scholar
  16. 16.
    National Library of Medicine, PubMed, Internet, http://www.ncbi.nlm.nih.gov/PubMed
  17. 17.
    National Library of Medicine, Unified Medical Language System (UMLS), Internet, http://www.nlm.nih.gov/research/umls/
  18. 18.
    Ward, G.: A Set of Lexical Resources, Internet, http://www.dcs.shef.ac.uk/research/ilash/Moby/

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • David B. Bracewell
    • 1
  • Scott Russell
    • 1
  • Annie S. Wu
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of Central FloridaOrlando

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