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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Brutlag, J., Meek, C.: Challenges of the Email Domain for Text Classification. In: International Conference on Machine Learning - ICML, pp. 103–110 (2000)
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)
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)
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)
Salton, G., Wong, A., Yang, C.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18(11), 613–620 (1975)
Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)
McCallum, A.: Mallet: A machine learning for language toolkit (2002), http://mallet.cs.umass.edu
Chang, C., Lin, C.: LIBSVM: A Library for Support Vector Machines. ACM Trans. on Intelligent Systems and Technology 2(3), 1–39 (2011)
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)
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)
Das, S.: Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection. In: International Conference on Machine Learning - ICML, pp. 74–81 (1994)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.): Feature Extraction, Foundations and Applications. Springer, Heidelberg (2006)
Joachims, T.: Learning to Classify Text Using Support Vector Machines. Kluwer Academic Publishers (2001)
Ferreira, A., Figueiredo, M.: Feature Transformation and Reduction for Text Classification. In: International Workshop on Pattern Recognition in Information Systems, pp. 72–81 (2010)
Cover, T., Thomas, J.: Elements of Information Theory. John Wiley & Sons (1991)
Bishop, C.: Pattern Recognition and Machine Learning, 1st edn. Springer, Heidelberg (2006)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tam, T., Ferreira, A., Lourenço, A. (2012). Automatic Foldering of Email Messages:A Combination Approach. In: Baeza-Yates, R., et al. Advances in Information Retrieval. ECIR 2012. Lecture Notes in Computer Science, vol 7224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28997-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-28997-2_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28996-5
Online ISBN: 978-3-642-28997-2
eBook Packages: Computer ScienceComputer Science (R0)