A comparison of event models for Naive Bayes text classification Export

@inproceedings{eventmodels, abstract = {Recent work in text classification has used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others use a multinomial model, that is, a uni-gram language model with integer word counts (e.g. Lewis and Gale 1994; Mitchell 1997). This paper aims to...}, author = {Mccallum, A. and Nigam, K.}, booktitle = {AAAI Workshop on "Learning for Text Categorization"}, citeulike-article-id = {2882579}, citeulike-linkout-0 = {http://citeseer.ist.psu.edu/55413.html}, citeulike-linkout-1 = {http://citeseer.lcs.mit.edu/55413.html}, citeulike-linkout-2 = {http://citeseer.ifi.unizh.ch/55413.html}, citeulike-linkout-3 = {http://citeseer.comp.nus.edu.sg/55413.html}, keywords = {language\_model}, posted-at = {2008-06-11 13:02:05}, priority = {0}, title = {A comparison of event models for Naive Bayes text classification}, url = {http://citeseer.ist.psu.edu/55413.html}, year = {1998} }

TY - CONF ID - eventmodels L3 - citeulike-article-id:2882579 N2 - Recent work in text classification has used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others use a multinomial model, that is, a uni-gram language model with integer word counts (e.g. Lewis and Gale 1994; Mitchell 1997). This paper aims to... TI - A comparison of event models for Naive Bayes text classification T2 - AAAI Workshop on "Learning for Text Categorization" KW - language_model AU - Mccallum, A AU - Nigam, K PY - 1998/// UR - http://citeseer.ist.psu.edu/55413.html ER -