Email overload: exploring personal information management of email
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A hybrid learning system for recognizing user tasks from desktop activities and email messages
Proceedings of the 11th international conference on Intelligent user interfaces
Using web browser interactions to predict task
Proceedings of the 15th international conference on World Wide Web
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Using task context to achieve effective information delivery
Proceedings of the 1st Workshop on Context, Information and Ontologies
Supervision of learning methods in user data interpretation
Proceedings of the 4th Information Interaction in Context Symposium
The role of current working context in professional search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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This paper is about using existing directory structures on the file system as models for e-mail classification. This is motivated by the aim to reduce the effort for users to organize their information flow. Classifiers were trained on categorized documents and tested on their performance on an unstructured set of e-mail correspondence related to the documents. Even though the documents and e-mails in our corpus belonged to the same categories, the classifiers showed very low accuracy on e-mail classification. More importantly, a learning curve experiment showed that initiating a model with documents can have a negative impact on the overall accuracy that could be achieved on e-mail classification. Features important for e-mail classification are inherently different than those important for document classification.