This paper presents an approach to the topic extraction from text documents using probabilistic graphical models. Multiple-cause networks with latent variables are used and the Helmholtz machines are utilized to ease the learning and inference. The learning in this model is conducted in a purely data-driven way and does not require prespecified categories of the given documents. Topic words extraction experiments on the TDT-2 collection are presented. Especially, document clustering results on a subset of TREC-8 ad-hoc task data show the substantial reduction of the inference time without significant deterioration of performance.
['Jeong-Ho Chang', 'Jae Won Lee', 'Yu-Seop Kim', 'Byoung-Tak Zhang']
Topic extraction from text documents using multiple-cause networks