Probabilistic term variant generator for biomedical terms

ABSTRACT

This paper presents an algorithm to generate possible variants for biomedical terms. The algorithm gives each variant its generation probability representing its plausibility, which is potentially useful for query and dictionary expansions. The probabilistic rules for generating variants are automatically learned from raw texts using an existing abbreviation extraction technique. Our method, therefore, requires no linguistic knowledge or labor-intensive natural language resource. We conducted an experiment using 83,142 MEDLINE abstracts for rule induction and 18,930 abstracts for testing. The results indicate that our method will significantly increase the number of retrieved documents for long biomedical terms.

REFERENCES

• 1: Marco Baroni , Johannes Matiasek , Harald Trost, Unsupervised discovery of morphologically related words based on orthographic and semantic similarity, Proceedings of the ACL-02 workshop on Morphological and phonological learning, p.48-57, July 11, 2002 [doi>10.3115/1118647.1118653]
• 2: David A. Hull, Stemming algorithms: a case study for detailed evaluation, Journal of the American Society for Information Science, v.47 n.1, p.70-84, Jan. 1996 [doi>10.1002/(SICI)1097-4571(199601)47:1<70::AID-ASI7>3.3.CO;2-Q]
• 3: Christian Jacquemin, Guessing morphology from terms and corpora, Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval, p.156-165, July 27-31, 1997, Philadelphia, Pennsylvania, United States
• 4: C. Jacquemin and E. Tzoukermann. NLP for term variant extraction: Synergy between Morphology, Lexicon and Syntax, pages 25--74. Kluwer Academic Publishers, 1999.
• 5: Jun'ichi Kazama , Takaki Makino , Yoshihiro Ohta , Jun'ichi Tsujii, Tuning support vector machines for biomedical named entity recognition, Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain, p.1-8, July 11-11, 2002, Phildadelphia, Pennsylvania [doi>10.3115/1118149.1118150]
• 6: J. D. Kim and J. Tsujii. Corpus-based approach to biological entity recognition. In Proceedings of the Second Meeting of the Special Interest Group on Text Data Mining of ISMB 2002, 2002.
• 7: M. Krauthammer, A. Rzhetsky, P. Morozov, and C. Friedman. Using blast for identifying gene and protein names in journal articles. GENE, 259:245--252, 2000.
• 8: Robert Krovetz, Viewing morphology as an inference process, Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval, p.191-202, June 27-July 01, 1993, Pittsburgh, Pennsylvania, United States [doi>10.1145/160688.160718]
• 9: Gonzalo Navarro, A guided tour to approximate string matching, ACM Computing Surveys (CSUR), v.33 n.1, p.31-88, March 2001 [doi>10.1145/375360.375365]
• 10: T. Ohta, Y. Tateisi, J.-D. Kim, and J. Tsujii. Genia corpus: an annotated research abstract corpus in molecular biology domain. In Proceedings of the Human Language Technology Conference (HLT 2002), 2002.
• 11: T. Ono, H. Hishigaki, A. Tanigami, and T. Takagi. Automated extraction of information on protein-protein interactions from the biological literature. BIOINFORMATICS, 17(2):155--161, 2001.
• 12: A. Schwartz and M. Hearst. A simple algorithm for identifying abbreviation definitions in biomedical texts,. In Proceedings of the Pacific Symposium on Biocomputing (PSB 2003), 2003.
• 13: Koichi Takeuchi , Nigel Collier, Use of support vector machines in extended named entity recognition, proceeding of the 6th conference on Natural language learning, p.1-7, August 31, 2002 [doi>10.3115/1118853.1118882]
• 14: Evelyne Tzoukermann , Judith L. Klavans , Christian Jacquemin, Effective use of natural language processing techniques for automatic conflation of multi-word terms: the role of derivational morphology, part of speech tagging, and shallow parsing, Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval, p.148-155, July 27-31, 1997, Philadelphia, Pennsylvania, United States
• 15: Jinxi Xu , W. Bruce Croft, Corpus-based stemming using cooccurrence of word variants, ACM Transactions on Information Systems (TOIS), v.16 n.1, p.61-81, Jan. 1998 [doi>10.1145/267954.267957]
• 16: Pierre Zweigenbaum , Natalia Grabar, Automatic Acquisition of Morphological Knowledge for Medical Language Processing, Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, p.416-422, June 01, 1999

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Language generation

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Thesauruses

General Terms:
Algorithms, Theory

Keywords:
dictionary expansion, query expansion, spelling variation