Tackling the Poor Assumptions of Naive Bayes Text Classifiers Export

@inproceedings{citeulike:3145766, 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 e\#ciency 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. We find that our simple corrections result in a fast algorithm that is competitive with stateof-the-art text classification algorithms such as the Support Vector Machine.}, author = {Rennie, Jason D. M. and Teevan, Jaime and Karger, David R.}, booktitle = {In Proceedings of the Twentieth International Conference on Machine Learning}, citeulike-article-id = {3145766}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.322}, keywords = {bayes, classification, naive, text}, pages = {616--623}, posted-at = {2009-01-28 21:01:29}, priority = {2}, title = {Tackling the Poor Assumptions of Naive Bayes Text Classifiers}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.322}, year = {2003} }

TY - CONF ID - citeulike:3145766 L3 - citeulike-article-id:3145766 N2 - Naive Bayes is often used as a baseline in text classification because it is fast and easy to implement. Its severe assumptions make such e#ciency 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. We find that our simple corrections result in a fast algorithm that is competitive with stateof-the-art text classification algorithms such as the Support Vector Machine. EP - 623 TI - Tackling the Poor Assumptions of Naive Bayes Text Classifiers T2 - In Proceedings of the Twentieth International Conference on Machine Learning SP - 616 KW - bayes KW - classification KW - naive KW - text AU - Rennie, Jason AU - Teevan, Jaime AU - Karger, David PY - 2003/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.322 ER -