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.

• Type: Conference
• Year: 2007
• Where: EMNLP
• Authors: Issei Sato, Hiroshi Nakagawa
• Keywords: EMNLP 2007, Models Multiple-topic Documents, Multiple-topic Documents, Natural Language Processing, Probabilistic Generative Model