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Automatic Foldering of Email Messages:A Combination Approach

  • Tony Tam
  • Artur Ferreira
  • André Lourenço
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7224)

Abstract

Automatic organization of email messages into folders is both an open problem and challenge for machine learning techniques. Besides the effect of email overload, which affects many email users worldwide, there are some increasing difficulties caused by the semantics applied by each user. The varying number of folders and their meaning are personal and in many cases pose difficulties to learning methods. This paper addresses automatic organization of email messages into folders, based on supervised learning algorithms. The textual fields of the email message (subject and body) are considered for learning, with different representations, feature selection methods, and classifiers. The participant fields are embedded into a vector-space model representation. The classification decisions from the different email fields are combined by majority voting. Experiments on a subset of the Enron Corpus and on a private email data set show the significant improvement over both single classifiers on these fields as well as over previous works.

Keywords

Automatic Foldering Email Classification Feature Weighting Feature Selection Supervised Learning 

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References

  1. 1.
    Whittaker, S., Sidner, C.: Email overload - exploring personal information management of email. In: ACM Conference on Human Factors in Computing Systems, pp. 276–283 (1996)Google Scholar
  2. 2.
    Brutlag, J., Meek, C.: Challenges of the Email Domain for Text Classification. In: International Conference on Machine Learning - ICML, pp. 103–110 (2000)Google Scholar
  3. 3.
    Bekkerman, R., Mccallum, A., Huang, G.: Automatic Categorization of Email into Folders: Benchmark Experiments on Enron and SRI Corpora. Technical report, University of Massachusetts (2004)Google Scholar
  4. 4.
    Klimt, B., Yang, Y.: The Enron Corpus: A New Dataset for Email Classification Research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS(LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Roth, M., Barenholz, T., Ben-David, A., Deutscher, D., Flysher, G., Hassidim, A., Horn, I., Leichtberg, A., Leiser, N., Matias, Y., Merom, R.: Suggesting (More) Friends Using the Implicit Social Graph. In: International Conference on Machine Learning - ICML, pp. 233–241 (2011)Google Scholar
  6. 6.
    Salton, G., Wong, A., Yang, C.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18(11), 613–620 (1975)zbMATHCrossRefGoogle Scholar
  7. 7.
    Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)Google Scholar
  8. 8.
    McCallum, A.: Mallet: A machine learning for language toolkit (2002), http://mallet.cs.umass.edu
  9. 9.
    Chang, C., Lin, C.: LIBSVM: A Library for Support Vector Machines. ACM Trans. on Intelligent Systems and Technology 2(3), 1–39 (2011)Google Scholar
  10. 10.
    Liu, L., Kang, J., Yu, J., Wang, Z.: A Comparative Study on Unsupervised Feature Selection Methods for Text Clustering. In: Int. Conference on Natural Language Processing and Knowledge Engineering, pp. 597–601. IEEE (2005)Google Scholar
  11. 11.
    Liu, H., Yu, L.: Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17(4), 491–502 (2005)CrossRefGoogle Scholar
  12. 12.
    Das, S.: Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection. In: International Conference on Machine Learning - ICML, pp. 74–81 (1994)Google Scholar
  13. 13.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)zbMATHGoogle Scholar
  14. 14.
    Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.): Feature Extraction, Foundations and Applications. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  15. 15.
    Joachims, T.: Learning to Classify Text Using Support Vector Machines. Kluwer Academic Publishers (2001)Google Scholar
  16. 16.
    Ferreira, A., Figueiredo, M.: Feature Transformation and Reduction for Text Classification. In: International Workshop on Pattern Recognition in Information Systems, pp. 72–81 (2010)Google Scholar
  17. 17.
    Cover, T., Thomas, J.: Elements of Information Theory. John Wiley & Sons (1991)Google Scholar
  18. 18.
    Bishop, C.: Pattern Recognition and Machine Learning, 1st edn. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  19. 19.
    Wang, S., Li, D., Song, X., Wei, Y., Li, H.: A feature selection method based on improved fisher’s discriminant ratio for text sentiment classification. Expert Systems with Applications 38, 8696–8702 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tony Tam
    • 1
  • Artur Ferreira
    • 1
    • 2
  • André Lourenço
    • 1
    • 2
  1. 1.Instituto Superior de Engenharia de LisboaLisboaPortugal
  2. 2.Instituto de TelecomunicaçõesLisboaPortugal

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