CONF keller:pascal-wshp:2004/IDIAP Theme Topic Mixture Model: A Graphical Model for Document Representation Keller, Mikaela Bengio, Samy EXTERNAL http://publications.idiap.ch/attachments/papers/2004/keller-pascal-wshp-2004.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/keller:rr04-05 Related documents Pascal Workshop on Text Mining and Understanding 2004 IDIAP-RR 04-05 Documents are usually represented in the bag-of-word space. However, this representation does not take into account the possible relations between words. We propose here a graphical model for representing documents: the Theme Topic Mixture Model (TTMM). This model assumes two types of relations among textual data. Topics link words to each other and Themes gather documents with particular distribution over the topics. This paper defines the TTMM, compares it to the related Latent Dirichlet Allocation (LDA) model (Blei, 2003) and reports some interesting empirical results. REPORT keller:rr04-05/IDIAP Theme Topic Mixture Model: A Graphical Model for Document Representation Keller, Mikaela Bengio, Samy EXTERNAL http://publications.idiap.ch/attachments/reports/2004/keller-idiap-rr-04-05.pdf PUBLIC Idiap-RR-05-2004 2004 IDIAP Published in PASCAL Workshop on Text Mining and Understanding, january 2004 Documents are usually represented in the bag-of-word space. However, this representation does not take into account the possible relations between words. We propose here a graphical model for representing documents: the Theme Topic Mixture Model (TTMM). This model assumes two types of relations among textual data. Topics link words to each other and Themes gather documents with particular distribution over the topics. This paper defines the TTMM, compares it to the related Latent Dirichlet Allocation (LDA) model (Blei, 2003) and reports some interesting empirical results.