Comparing Manual Text Patterns and Machine Learning for Classification of E-Mails for Automatic Answering by a Government Agency
2011 (English)In: Computational Linguistics and Intelligent Text Processing: Proceedings, Part II / [ed] Alexander Gelbukh, Springer-Verlag Berlin Heidelberg , 2011Conference paper (Other academic)
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
automatic e-mail answering, text pattern matching, machine learning, SVM, Naïve Bayes, E-government
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-55286DOI: 10.1007/978-3-642-19437-5_19OAI: oai:DiVA.org:su-55286DiVA: diva2:402271
12th International Conference, CICLing 2011, Tokyo, Japan, February 20-26, 2011.