A Comparison of Methods for Automatic Term Extraction for Domain Analysis

  • William B. Frakes
  • Gregory Kulczycki
  • Jason Tilley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8919)


Fourteen word frequency metrics were tested to evaluate their effectiveness in identifying vocabulary in a domain. Fifteen domain-engineering projects were examined to measure how closely the vocabularies selected by the fourteen word frequency metrics were to the vocabularies produced by domain engineers. Stemming and stopword removal were also evaluated to measure their impact on selecting proper vocabulary terms. The results of the experiment show that stemming and stopword removal do improve performance and that term frequency is a valuable contributor to performance. Most word frequency metrics gave similar results. A few of the metrics did poorly compared to the others.


domain engineering vocabulary extraction stemming stoplists word frequency metrics software reuse domain documents 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Crawley, M.J.: The R Book. Wiley, West Sussex (2007)CrossRefMATHGoogle Scholar
  2. 2.
    Frakes, W.: A Method for Bounding Domains. In: IASTED International Conference Software Engineering and Applications, Las Vegas, NV, pp. 269–272 (2000)Google Scholar
  3. 3.
    Frakes, W.B.: Stemming Algorithms. In: Frakes, W.B., Baeza-Yates, R. (eds.) Information Retrieval: Data Structures and Algorithms, pp. 131–160. Prentice Hall, Englewood Cliffs (1992)Google Scholar
  4. 4.
    Frakes, W.B., Kang, K.: Software Reuse Research: Status and Future. IEEE Transactions on Software Engineering 31(7), 529–536 (2005)CrossRefGoogle Scholar
  5. 5.
    Frakes, W., Prieto-Diaz, R., Fox, C.: DARE: Domain Analysis and Reuse Environment. Annals of Software Engineering, 125–141 (1998)Google Scholar
  6. 6.
    Justeson, J., Katz, S.: Technical Terminology: Some Linguistic Properties and an Algorithm for Identification in Text. In: Natural Language Engineering, pp. 9–27. IBM Research Division, Almadem (1993)Google Scholar
  7. 7.
    Luhn, H.P.: The Automatic Creation of Literature Abstracts. IBM Journal of Research and Development 2(2), 159–165 (1958)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Noreault, T., McGill, M., Koll, M.: A performance evaluation of similarity measures, document term weighting schemes and representations in a Boolean environment. In: Proceedings of the 3rd Annual ACM Conference on Research and Development in Information Retrieval, pp. 57–76. Butterworth and Co., Cambridge (1980)Google Scholar
  9. 9.
    Porter, M.F.: An Algorithm for Suffix Striping. Program 14(3), 130–137 (1980)CrossRefGoogle Scholar
  10. 10.
    Sclano, F., Velardi, P.: TermExtractor: A Web Application to Learn the Shared Terminology of Emergent Web Communities. In: Gonçalves, R.J., Müller, J.P., Mertins, K., Zelm, M. (eds.) Enterprise Interoperability II, pp. 287–290. Springer, London (2007)CrossRefGoogle Scholar
  11. 11.
    Tilley, J.: A Comparison of Statistical Filtering Methods for Automatic Term Extraction for Domain Analysis. Masters Thesis, Computer Science Department, Virginia Tech (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • William B. Frakes
    • 1
  • Gregory Kulczycki
    • 1
  • Jason Tilley
    • 1
  1. 1.Software Reuse LaboratoryVirginia TechFalls ChurchUSA

Personalised recommendations