Name
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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1076034.1076094 | SIGIR |
Keywords | Field | DocType |
machine learning,statistical significance,maximum entropy model,bag of words | Collective classification,Data mining,Email management,Computer science,Thread (computing),Correlation,Artificial intelligence,Principle of maximum entropy,Classifier (linguistics),Machine learning | Conference |
ISBN | Citations | PageRank |
1-59593-034-5 | 81 | 5.16 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vitor R. Carvalho | 1 | 672 | 36.38 |
| William W. Cohen | 2 | 10178 | 1243.74 |