Applying the Multiple Cause Mixture Model to Text Categorization
Abstract
This paper introduces the use of the Multiple Cause Mixture Model to automatic text category assignment. Although much research has been done on text categorization, this algorithm is novel in that is unsupervised, that is, does not require pre-labeled training examples, and it can assign multiple category labels to documents. In this paper we present very preliminary results of the application of this model to a standard test collection, evaluating it in supervised mode in order to facilitate comparison with other methods, and showing initial results of its use in unsupervised mode.
Cite
Text
Sahami et al. "Applying the Multiple Cause Mixture Model to Text Categorization." International Conference on Machine Learning, 1996.Markdown
[Sahami et al. "Applying the Multiple Cause Mixture Model to Text Categorization." International Conference on Machine Learning, 1996.](https://mlanthology.org/icml/1996/sahami1996icml-applying/)BibTeX
@inproceedings{sahami1996icml-applying,
title = {{Applying the Multiple Cause Mixture Model to Text Categorization}},
author = {Sahami, Mehran and Hearst, Marti A. and Saund, Eric},
booktitle = {International Conference on Machine Learning},
year = {1996},
pages = {435-443},
url = {https://mlanthology.org/icml/1996/sahami1996icml-applying/}
}