Nothing Special   »   [go: up one dir, main page]

Skip to main content

Concept Indexing for Automated Text Categorization

  • Conference paper
Natural Language Processing and Information Systems (NLDB 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3136))

Abstract

In this paper we explore the potential of concept indexing with WordNet synsets for Text Categorization, in comparison with the traditional bag of words text representation model. We have performed a series of experiments in which we also test the possibility of using simple yet robust disambiguation methods for concept indexing, and the effectiveness of stoplist-filtering and stemming on the SemCor semantic concordance. Results are not conclusive yet promising.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34, 1–47 (2002)

    Article  MathSciNet  Google Scholar 

  2. Salton, G.: Automatic text processing: the transformation, analysis, and retrieval of information by computer. Addison-Wesley, Reading (1989)

    Google Scholar 

  3. Caropreso, M., Matwin, S., Sebastiani, F.: A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization. In: Text Databases and Document Management: Theory and Practice, pp. 78–102. Idea Group Publishing, USA (2001)

    Google Scholar 

  4. Lewis, D.D.: Representation and learning in information retrieval. PhD thesis, Department of Computer Science, University of Massachusetts, Amherst, US (1992)

    Google Scholar 

  5. Riloff, E.: Using learned extraction patterns for text classification. In: Connectionist, statistical, and symbolic approaches to learning for natural language processing, pp. 275–289. Springer, Heidelberg (1996)

    Google Scholar 

  6. Fukumoto, F., Suzuki, Y.: Learning lexical representation for text categorization. In: Proceedings of the NAACL 2001 Workshop on WordNet and Other Lexical Resources (2001)

    Google Scholar 

  7. Scott, S.: Feature engineering for a symbolic approach to text classification. Master’s thesis, Computer Science Dept., University of Ottawa, Ottawa, CA (1998)

    Google Scholar 

  8. Gonzalo, J., Verdejo, F., Chugur, I., Cigarrán, J.: Indexing with WordNet synsets can improve text retrieval. In: Proceedings of the COLING/ACL Workshop on Usage of WordNet in Natural Language Processing Systems (1998)

    Google Scholar 

  9. Junker, M., Abecker, A.: Exploiting thesaurus knowledge in rule induction for text classification. In: Proceedings of the, 2nd International Conference on Recent Advances in Natural Language Processing, pp. 202–207 (1997)

    Google Scholar 

  10. Liu, J., Chua, T.: Building semantic perceptron net for topic spotting. In: Proceedings of 37th Meeting of Association of Computational Linguistics (2001)

    Google Scholar 

  11. Petridis, V., Kaburlasos, V., Fragkou, P., Kehagias, A.: Text classification using the σ-FLNMAP neural network. In: Proceedings of the 2001 International Joint Conference on Neural Networks (2001)

    Google Scholar 

  12. Cavnar, W., Trenkle, J.: N-gram-based text categorization. In: Proceedings of the 3rd Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, US, pp. 161–175 (1994)

    Google Scholar 

  13. Miller, G.A.: WordNet: A lexical database for English. Communications of the ACM 38, 39–41 (1995)

    Article  Google Scholar 

  14. Voorhees, E.: Using WordNet for text retrieval. In: WordNet: An Electronic Lexical Database, MIT Press, Cambridge (1998)

    Google Scholar 

  15. Mihalcea, R., Moldovan, D.: Semantic indexing using WordNet senses. In: Proceedings of ACL Workshop on IR and NLP (2000)

    Google Scholar 

  16. Stokoe, C., Oakes, M.P., Tait, J.: Word sense disambiguation in information retrieval revisited. In: Proceedings of the 26th ACM International Conference on Research and Development in Information Retrieval (2003)

    Google Scholar 

  17. Kilgarriff, A., Rosenzweig, J.: Framework and results for english SENSEVAL. Computers and the Humanities 34, 15–48 (2000)

    Article  Google Scholar 

  18. Miller, G.A., Leacock, C., Tengi, R., Bunker, R.: A semantic concordance. In: Proc. Of the ARPA Human Language Technology Workshop, pp. 303–308 (1993)

    Google Scholar 

  19. Kessler, B., Nunberg, G., Schütze, H.: Automatic detection of text genre. In: Proceedings of ACL 1997, 35th Annual Meeting of the Association for Computational Linguistics, Madrid, ES, pp. 32–38 (1997)

    Google Scholar 

  20. Yang, Y., Pedersen, J.: A comparative study on feature selection in text categorization. In: Proc. Of the 14th International Conf. On Machine Learning (1997)

    Google Scholar 

  21. Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Shavlik, J. (ed.) Machine Learning: Proceedings of the Fifteenth International Conference, San Francisco, CA, Morgan Kaufmann Publishers, San Francisco (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gómez, J.M., Cortizo, J.C., Puertas, E., Ruiz, M. (2004). Concept Indexing for Automated Text Categorization. In: Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2004. Lecture Notes in Computer Science, vol 3136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27779-8_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-27779-8_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22564-5

  • Online ISBN: 978-3-540-27779-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics