6,253 research outputs found
Bernard Merialdo
No full textIn 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 based on keywords. On these cases, the probabilistic approach gives better recall and precision indicators
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