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A Term Normalization Method for Better Performance of Terminology Construction

  • Myunggwon Hwang
  • Do-Heon Jeong
  • Hanmin Jung
  • Won-Kyoung Sung
  • Juhyun Shin
  • Pankoo Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7267)

Abstract

The importance of research on knowledge management is growing due to recent issues with big data. The most fundamental steps in knowledge management are the extraction and construction of terminologies. Terms are often expressed in various forms and the term variations play a negative role, becoming an obstacle which causes knowledge systems to extract unnecessary knowledge. To solve the problem, we propose a method of term normalization which finds a normalized form (original and standard form defined in dictionaries) of variant terms. The method employs a couple of characteristics of terms: one is appearance similarity, which measures how similar terms are, and the other is context similarity which measures how many clue words they share. Through experiment, we show its positive influence of both similarities in the term normalization.

Keywords

Term Normalization Terminology Appearance Similarity 

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References

  1. 1.
    Dowdal, J., Rinaldi, F., Ibekwe-SanJuan, F., SanJuan, E.: Complex Structuring of Term Variants for Question Answering. In: Proc. of the ACM Workshop on Multiword Expressions: Analysis, Acquisition and Treatment, vol. 18, pp. 1–8 (2003)Google Scholar
  2. 2.
    Ibekwe-Sanjuan, F.: Terminological Variation, a Means of Identifying Research Topics from Texts. In: Proc. of Intl. Conf. on Computational Linguistics, vol. 1, pp. 564–570 (1998)Google Scholar
  3. 3.
    Porter, M.F.: An algorithm for suffix stripping. J. of Program 14(3), 130–137 (1980)CrossRefGoogle Scholar
  4. 4.
    Toutanova, K., Manning, C.: Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger. In: Proc. Joint SIGDAT Conf. Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 63–70 (2000)Google Scholar
  5. 5.
    Hwang, M., Kim, P.: A New Similarity Measure for Automatic Construction of the Unknown Word Lexical Dictionary. Intl. J. on Semantic Web and Information Systems (IJSWIS) 5(1), 48–64 (2009)CrossRefGoogle Scholar
  6. 6.
    Hwang, M., Choi, C., Kim, P.: Automatic Enrichment of Semantic Relation Networks and its Application to Word Sense Disambiguation. IEEE Transactions on Knowledge and Data Engineering 23(6), 845–858 (2011)CrossRefGoogle Scholar
  7. 7.
    Brank, J., Mladenic, D., Grobelnik, M., Milic-Frayling, N.: Feature Selection for the Classification of Large Document Collections. Journal of Universal Computer Science 14(10), 1562–1596 (2008)MathSciNetGoogle Scholar
  8. 8.
    Duong, T.H., Jo, G., Jung, J.J., Nguyen, N.T.: Complexity Analysis of Ontology Integration Methodologies: A Comparative Study. Journal of Universal Computer Science 15(4), 877–897 (2009)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Jung, J.J.: Semantic business process integration based on ontology alignment. Expert Systems with Applications 36(8), 11013–11020 (2009)CrossRefGoogle Scholar
  10. 10.
    Hwang, M., Choi, D., Choi, J., Kim, H., Kim, P.: Similarity Measure for Semantic Document Interconnections. Information-An International Interdisciplinary Journal 13(2), 253–267 (2010)Google Scholar
  11. 11.
    Hwang, M., Choi, D., Kim, P.: A Method for Knowledge Base Enrichment using Wikipedia Document Information. Information-An International Interdisciplinary Journal 13(5), 1599–1612 (2010)Google Scholar
  12. 12.
    Bawakid, A., Oussalah, M.: Using features extracted from Wikipedia for the task of Word Sense Disambiguation. In: Proc. of IEEE Intl. Conf. on Cybernetic Intelligent Systems, pp. 1–6 (2010)Google Scholar
  13. 13.
    Fogarolli, A.: Word Sense Disambiguation Based on Wikipedia Link Structure. In: Proceedings of IEEE Intl. Conf. on Semantic Computing, pp. 77–82 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Myunggwon Hwang
    • 1
  • Do-Heon Jeong
    • 1
  • Hanmin Jung
    • 1
  • Won-Kyoung Sung
    • 1
  • Juhyun Shin
    • 2
  • Pankoo Kim
    • 2
  1. 1.Korea Institute of Science and Technology Information (KISTI)DaejeonSouth Korea
  2. 2.Chosun UniversityGwangjuSouth Korea

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