Demand based approach to control data load on email servers
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
A multi-classifier system for text categorization
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
An ontology-based mechanism for automatic categorization of web services
Concurrency and Computation: Practice & Experience
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The continuing explosive growth of textual content within the World Wide Web has given rise to the need for sophisticated Text Classification (TC) techniques that combine efficiency with high quality of results. E-mail filtering is one application that has the potential to affect every user of the internet. Even though a large body of research has delved into this problem, there is a paucity of survey that indicates trends and directions. This paper attempts to categorize the prevalent popular techniques for classifying email as spam or legitimate and suggest possible techniques to fill in the lacunae. Our findings suggest that context-based email filtering has the most potential in improving quality by learning various contexts such as n-gram phrases, linguistic constructs or users’ profile based context to tailor his/her filtering scheme.