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Computational Linguistics

Hwee Tou Ng, Editor
December 2009, Vol. 35, No. 4, Pages 597-635
(doi: 10.1162/coli.2009.35.4.35404)
© 2009 Association for Computational Linguistics
An Empirical Study of Corpus-Based Response Automation Methods for an E-mail-Based Help-Desk Domain
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This article presents an investigation of corpus-based methods for the automation of help-desk e-mail responses. Specifically, we investigate this problem along two operational dimensions: (1) information-gathering technique, and (2) granularity of the information. We consider two information-gathering techniques (retrieval and prediction) applied to information represented at two levels of granularity (document-level and sentence-level). Document-level methods correspond to the reuse of an existing response e-mail to address new requests. Sentence-level methods correspond to applying extractive multi-document summarization techniques to collate units of information from more than one e-mail. Evaluation of the performance of the different methods shows that in combination they are able to successfully automate the generation of responses for a substantial portion of e-mail requests in our corpus. We also investigate a meta-selection process that learns to choose one method to address a new inquiry e-mail, thus providing a unified response automation solution.