Stanford InfoLab Publication Server

Applying the Multiple Cause Mixture Model to Text Categorization

Sahami, Mehran and Hearst, Marti and Saund, Eric (1996) Applying the Multiple Cause Mixture Model to Text Categorization. In: 1996 AAAI Spring Symposium on Machine Learning in Information Access..




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.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Previous number = SIDL-WP-1996-0033
Subjects:Computer Science > Digital Libraries
Projects:Digital Libraries
Related URLs:Project Homepage
ID Code:209
Deposited By:Import Account
Deposited On:28 Oct 2001 16:00
Last Modified:09 Dec 2008 09:34

Download statistics

Repository Staff Only: item control page