Using Semantic Features to Improve Task Identification in Email Messages

Authors:
Shinjae Yoo;Donna Gates;Lori Levin;Simon Fung;Sachin Agarwal;Michael Freed
Affiliations:
Language Technology Institute, Pittsburgh, PA 15213;Language Technology Institute, Pittsburgh, PA 15213;Language Technology Institute, Pittsburgh, PA 15213;Language Technology Institute, Pittsburgh, PA 15213;Language Technology Institute, Pittsburgh, PA 15213;SRI International, Menlo Park CA 94025
Venue:
NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
Year:
2008

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Abstract

Automated identification of tasks in email messages can be very useful to busy email users. What constitutes a task varies across individuals and must be learned for each user. However, training data for this purpose tends to be scarce. This paper addresses the lack of training data using domain-specific semantic features in document representation for reducing vocabulary mismatches and enhancing the discriminative power of trained classifiers when the number of training examples is relatively small.