Automatic Foldering of Email Messages:A Combination Approach
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.
KeywordsAutomatic Foldering Email Classification Feature Weighting Feature Selection Supervised Learning
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- 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.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.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
- 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
- 7.Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)Google Scholar
- 8.McCallum, A.: Mallet: A machine learning for language toolkit (2002), http://mallet.cs.umass.edu
- 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.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
- 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
- 15.Joachims, T.: Learning to Classify Text Using Support Vector Machines. Kluwer Academic Publishers (2001)Google Scholar
- 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.Cover, T., Thomas, J.: Elements of Information Theory. John Wiley & Sons (1991)Google Scholar