Theme Topic Mixture Model: A Graphical Model for Document Representation

@techreport{citeulike:432468, abstract = {Documents are usually represented in the bag-of-word space. However, this repre- sentation 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 denes the TTMM, compares it to the related Latent Dirichlet Allocation (LDA) model [?] and reports some interesting empirical results.}, author = {Keller, Mikaela and Bengio, Samy }, citeulike-article-id = {432468}, journal = {IDIAP-RR 04-05}, month = {April}, posted-at = {2006-01-05 11:05:45}, priority = {4}, title = {Theme Topic Mixture Model: A Graphical Model for Document Representation}, url = {ftp://ftp.idiap.ch/pub/reports/2004/rr04-05.pdf}, year = {2005} }

TY - RPRT ID - citeulike:432468 L3 - citeulike-article-id:432468 TI - Theme Topic Mixture Model: A Graphical Model for Document Representation JF - IDIAP-RR 04-05 N2 - Documents are usually represented in the bag-of-word space. However, this repre- sentation 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 denes the TTMM, compares it to the related Latent Dirichlet Allocation (LDA) model [?] and reports some interesting empirical results. AU - Keller, Mikaela AU - Bengio, Samy PY - 2005/04/01/ UR - ftp://ftp.idiap.ch/pub/reports/2004/rr04-05.pdf ER -