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Comparing Manual Text Patterns and Machine Learning for Classification of E-Mails for Automatic Answering by a Government Agency
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2011 (English)In: Computational Linguistics and Intelligent Text Processing: Proceedings, Part II / [ed] Alexander Gelbukh, Springer-Verlag Berlin Heidelberg , 2011Conference paper (Other academic)
Abstract [en]

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 an- swerable 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.

Place, publisher, year, edition, pages
Springer-Verlag Berlin Heidelberg , 2011.
, Lecture Notes in Computer Science, ISSN 0302-9743 ; 6609
Keyword [en]
automatic e-mail answering, text pattern matching, machine learning, SVM, Naïve Bayes, E-government
National Category
Information Science
Research subject
Computer and Systems Sciences
URN: urn:nbn:se:su:diva-55286DOI: 10.1007/978-3-642-19437-5_19OAI: diva2:402271
12th International Conference, CICLing 2011, Tokyo, Japan, February 20-26, 2011.
Available from: 2011-03-07 Created: 2011-03-07 Last updated: 2011-03-08Bibliographically approved

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Dalianis, HerculesSneiders, Eriks
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ReferencesLink to record
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