On the collective classification of email "speech acts"

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 textclassification 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 improvement obtained by collective classification is appears to be consistent across email acts suggested by prior speech-act theory. Categories and Subject Descriptors I.2.6 [Articial Intelligence]: Learning; H.4.1 [Information Systems Applications]: Office Automation; I.5.4 [Pattern Recognition]: Applications. General Term...

• Type: Conference
• Year: 2005
• Where: SIGIR
• Authors: Vitor Rocha de Carvalho, William W. Cohen
• Keywords: Collective Classification, Email Acts, Email Messages, SIGIR 2005