| Abstract |
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We are developing a corpus-based approach for the prediction
of help-desk responses from features in customers’
emails, where responses are represented at two levels of
granularity: document and sentence. We present an automatic
evaluation of the responses generated by our system,
as well as a manual one involving human judges. The automatic
evaluation involves textual comparisons between generated
responses and responses composed by the help-desk
operators. The results show that both levels of granularity
produce good responses, addressing inquiries of different
kinds. The human-based evaluation measures response informativeness,
and confirms our conclusion that both levels
of granularity produce useful responses.
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 Additional Information
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Citation:
Ingrid Zukerman, Yuval Marom,
"A corpus-based approach to help-desk response generation,"
cimca,
p. 23,
International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06),
2006
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