We consider classification of email messages as to whether or not they contain certain "email acts", such as a request or a commitment. We show that exploiting the sequential correlation among email messages in the same thread can improve email-act classification. More specifically, we describe a new text-classification algorithm based on a dependency-network based collective classification method, in which the local classifiers are maximum entropy models based on words and certain relational features. We show that statistically significant improvements over a bag-of-words baseline classifier can be obtained for some, but not all, email-act classes. Performance improvements obtained by collective classification appears to be consistent across many email acts suggested by prior speech-act theory.
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