Abstract
E-mails to government institutions as well as to large companies may contain a large proportion of queries that can be answered in a uniform way. We analysed and manually annotated 4,404 e-mails from citizens to the Swedish Social Insurance Agency, and compared two methods for detecting answerable e-mails: manually-created text patterns (rule-based) and machine learning-based methods. We found that the text pattern-based method gave much higher precision at 89 percent than the machine learning-based method that gave only 63 percent precision. The recall was slightly higher (66 percent) for the machine learning-based methods than for the text patterns (47 percent). We also found that 23 percent of the total e-mail flow was processed by the automatic e-mail answering system.
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References
Busemann, S., Schmeier, S., Arens, R.G.: Message classification in the call center. In: Proceedings of the Sixth Conference on Applied Natural Language Processing, Seattle, Washington, pp. 158–165. ACL (2000)
Scheffer, T.: E-mail answering assistance by semi-supervised text classification. Intelligent Data Analysis 8(5), 481–493 (2004)
Lapalme, G., Kosseim, L.: Mercure: Towards an automatic e-mail follow-up system. IEEE Computational Intelligence Bulletin 2(1), 14–18 (2003)
Sneiders, E.: Automated E-mail Answering by Text Pattern Matching. In: Loftsson, H., Rögnvaldsson, E., Helgadóttir, S. (eds.) IceTAL 2010. LNCS, vol. 6233, pp. 381–392. Springer, Heidelberg (2010)
Dalianis, H., Rosell, M., Sneiders, E.: Clustering E-mails for the swedish social insurance agency – what part of the E-mail thread gives the best quality? In: Loftsson, H., Rögnvaldsson, E., Helgadóttir, S. (eds.) IceTAL 2010. LNCS, vol. 6233, pp. 115–120. Springer, Heidelberg (2010)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11( 1) (2005)
Cohn, D.A., Zoubin, G., Michael, I.J.: Active learning with statistical models. Journal of Artificial Intelligence Research 4, 129–145 (1996)
Lampert, A., Dale, R., Paris, C.: Detecting Emails Containing Requests for Action. In: The Proceeding of Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL-HLT, Los Angeles, pp. 984–992 (2010)
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Dalianis, H., Sjöbergh, J., Sneiders, E. (2011). Comparing Manual Text Patterns and Machine Learning for Classification of E-Mails for Automatic Answering by a Government Agency. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6609. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19437-5_19
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DOI: https://doi.org/10.1007/978-3-642-19437-5_19
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