Tackling the Poor Assumptions of Naive Bayes Text Classifiers

Jason D. Rennie, Lawrence Shih, Jaime Teevan, and David Karger

Naive Bayes is often used as a baseline text classiffication because it is fast and easy to implement. Its severe assumptions make such efficiency possible but also adversely affect the quality of its results. In this paper we propose simple, heuristic solutions to some the problems with Naive Bayes classifiers, addressing both systemic issues as well as problems that arise because text is not actually generated according to a multinomial model. We find that our simple corrections result in fast algorithm that is competitive with state-of-the-art text classification algorithms such as the Support Vector Machine.

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