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Abstract: In this paper, we propose and experiment a probabilistic approach to document classification. We consider the problem of automatically assigning a new article to a Usenet newsgroup. To model a newsgroup, we build a probabilistic language model which is supposed to generate articles for this newsgroup. When a new article is presented, we use a Maximum A Posteriori rule to decide if the message was generated by this newsgroup or not. We evaluate this approach and compare it to a classification... (Update)
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BibTeX entry: (Update)
@misc{ bp-bernard,
author = "Institut Eurecom Bp",
title = "Bernard Merialdo",
url = "citeseer.ist.psu.edu/759204.html" }
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