Knowledge-based processing of medical language: A language engineering approach

  • Martin Schröder
Technical Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 671)


In medicine large amounts of natural language documents have to be processed. Medical language is an interesting domain for the application of techniques developed in computational linguistics. Moreover, large scale applications of medical language processing raise the need to study the process of language engineering, which emphasizes some different problems than basic research. The texts found in medical applications show characteristics of a specific sublanguage that can be exploited for language processing. We present the Metexa system (Medical Text Analysis) for the analysis of radiological reports. To be able to process utterances of telegraphic style, the emphasis in system design has been put on semantic and knowledge processing components. However, a unification-based bottom-up parser is used to exploit syntactic information wherever possible. For semantic and knowledge representation a relevant part of the Conceptual Graph Theory by John Sowa has been implemented in order to yield a conceptual graph as the semantic representation of an utterance. This can be mapped e.g. to a database schema. A resolution-based inference procedure has been implemented to infer new facts from the analysed utterances.

Key words

Natural Language Processing Text Analysis Language Engineering Parsing Semantics Conceptual Graphs Medical Language Processing Sublanguage 


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  1. 1.
    James Allen. Natural Language Understanding. Benjamin/Cummings, Menlo Park, 1987.Google Scholar
  2. 2.
    R. H. Baud, A.-M. Rassinoux, and J.R. Scherrer. Knowledge representation of discharge summaries. In M. Stefanelli, A. Hasman, M. Fieschi, and J. Talmon, editors, AIME-91, volume 44 of Lecture Notes in Medial Informatics, pages 173–182, Berlin, 1991. Springer-Verlag.Google Scholar
  3. 3.
    Jean Fargues, Marie-Claude Landau, Anne Dugourd, and Laurent Catach. Conceptual graphs for semantics and knowledge processing. IBM Journal of Research and Development, 30(1):70–79, Jan 1986.Google Scholar
  4. 4.
    Gerald Gazdar and Christopher Mellish. Natural Language Processing in PRO-LOG. Addison-Wesley, Wokingham, England, 1989.Google Scholar
  5. 5.
    Gerhard Heyer. Elements of a natural language processing technology. In H. Haugeneder and G. Heyer, editors, Language Technology and Practice of NLP. Vieweg, 1992. (to appear).Google Scholar
  6. 6.
    Lawrence C. Kingsland, editor. The 13th Annual Symposium on Computer Applications in Medical Care. IEEE Computer Society Press, November 1989.Google Scholar
  7. 7.
    John Lehrberger. Sublanguage analysis. In Ralph Grishman and Richard Kittredge, editors, Analyzing Language in Restricted Domains: Sublanguage Description and Processing, pages 19–38. Lawrence Erlbaum Associates, Hillsdale, NJ, 1986.Google Scholar
  8. 8.
    Yuji Matsumoto, Hozumi Tanaka, Hideki Hirakawa, Hideo Miyoshi, and Hideki Yasukawa. BUP: A bottom-up parser embedded in Prolog. New Generation Computing, 1:145–158, 1983.Google Scholar
  9. 9.
    Alexa T. McCray. The UMLS Semantic Network. In Lawrence C. Kingsland, editor, The 13th Annual Symposium on Computer Applications in Medical Care, pages 503–507. IEEE Computer Society Press, November 1989.Google Scholar
  10. 10.
    Christian Mery, Bernhard Normier, and Antoine Ogonowski. “INTERMED”: A Medical Language Interface. In J. Fox, M. Fieschi, and R. Engelbrecht, editors, AIME 87, pages 3–8, Berlin, 1987. Springer-Verlag.Google Scholar
  11. 11.
    Makoto Nagao. Language engineering: The real bottle neck of natural language processing (panel). In COLING-88, pages 448–453, 1988.Google Scholar
  12. 12.
    Maria Teresa Pazienza and Paola Velardi. A structured representation of wordsenses for semantic analysis. In European ACL 87, pages 249–257, 1987.Google Scholar
  13. 13.
    Peter M. Pietrzyk. Survey of the Göttingen Medical Text Analysis System. In R. Hansen, B. G. Solheim, R. R. O'Moore, and F. H. Roger, editors, Medical Informatics Europe 88, volume 35 of Lecture Notes in Medical Informatics, pages 128–132, Berlin, 1988. Springer-Verlag.Google Scholar
  14. 14.
    Naomi Sager, Carol Friedman, and Margaret S. Lyman. Medical Language Processing: Computer Management of Narrative Data. Addison-Wesley, Reading, MA, 1987.Google Scholar
  15. 15.
    J. R. Scherrer, R. A. CÔté, and S. D. Mandil, editors. Computerized natural medical language processing for knowledge representation. North Holland, Amsterdam, 1989.Google Scholar
  16. 16.
    Martin Schröder. Bemerkungen zur Auswahl eines Wissensrepräsentationsformalismus für ein System zur wissensbasierten Textanalyse. In W. Hoeppner et al., editor, Materialien zum GWAI-91 Workshop “Wissensrepräsentation in natürlichsprachlichen Systemen”, Bonn, 1991.Google Scholar
  17. 17.
    Martin Schröder. Ein semantisch-gesteuerter Bottom-Up-Parser in Prolog. Mitteilung FBI-HH-M-195/91, Universität Hamburg, FB Informatik, Mai 1991.Google Scholar
  18. 18.
    Martin Schröder. Knowledge based analysis of radiology reports using conceptual graphs. In Proceedings of the 7th Annual Workshop on Conceptual Graphs, Las Cruces, New Mexico, 8.–10. July 1992. New Mexico State University.Google Scholar
  19. 19.
    Martin Schröder. Supporting speech processing by expectations: A conceptual model of radiological reports to guide the selection of word hypotheses. In KONVENS-92, 1. Konferenz “Verarbeitung natürlicher Sprache”, Informatik aktuell, Berlin, 1992. Springer-Verlag.Google Scholar
  20. 20.
    John F. Sowa. Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, IBM Systems Research Institute, 1984.Google Scholar
  21. 21.
    John F. Sowa. Knowledge representation in databases, expert systems, and natural language. In R. A. Meersman, Zh. Shi, and C-H. Kung, editors, Artificial Intelligence in Databases and Information Systems (DS-3), pages 17–50. North Holland Publ. Co., 1990.Google Scholar
  22. 22.
    John F. Sowa. Towards the expressive power of natural language. In John F. Sowa, editor, Principles of Semantic Networks: Explorations in the Representation of Knowledge. Morgan Kaufmann Publ., San Mateo, CA, 1991.Google Scholar
  23. 23.
    F. Wingert. Morphologic analysis of compound words. Methods of Information in Medicine, 24:155–162, 1985.PubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Martin Schröder
    • 1
  1. 1.Computer Science Department, Natural Language Systems (NatS)University of HamburgHamburg 50Germany

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