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- W2095688344 abstract "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." @default.
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- W2095688344 date "2005-08-15" @default.
- W2095688344 modified "2023-10-16" @default.
- W2095688344 title "On the collective classification of email speech acts" @default.
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- W2095688344 doi "https://doi.org/10.1145/1076034.1076094" @default.
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