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. © 2011 Springer-Verlag.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6609 LNCS, pp. 234–243). https://doi.org/10.1007/978-3-642-19437-5_19
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