Sciweavers

Share
EMNLP
2007

Bayesian Document Generative Model with Explicit Multiple Topics

9 years 2 months ago
Bayesian Document Generative Model with Explicit Multiple Topics
In this paper, we proposed a novel probabilistic generative model to deal with explicit multiple-topic documents: Parametric Dirichlet Mixture Model(PDMM). PDMM is an expansion of an existing probabilistic generative model: Parametric Mixture Model(PMM) by hierarchical Bayes model. PMM models multiple-topic documents by mixing model parameters of each single topic with an equal mixture ratio. PDMM models multiple-topic documents by mixing model parameters of each single topic with mixture ratio following Dirichlet distribution. We evaluate PDMM and PMM by comparing F-measures using MEDLINE corpus. The evaluation showed that PDMM is more effective than PMM.
Issei Sato, Hiroshi Nakagawa
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where EMNLP
Authors Issei Sato, Hiroshi Nakagawa
Comments (0)
books