Tackling the Poor Assumptions of Naive Bayes Text Classifiers (2003)
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| Venue: | In Proceedings of the Twentieth International Conference on Machine Learning |
| Citations: | 82 - 6 self |
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@INPROCEEDINGS{Rennie03tacklingthe,
author = {Jason D. M. Rennie and Lawrence Shih and Jaime Teevan and David R. Karger},
title = {Tackling the Poor Assumptions of Naive Bayes Text Classifiers},
booktitle = {In Proceedings of the Twentieth International Conference on Machine Learning},
year = {2003},
pages = {616--623}
}
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Abstract
Naive Bayes is often used as a baseline in text classification 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 of 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.
