Theme Topic Mixture Model: A Graphical Model for Document Representation. Keller, M. and Bengio, S. In PASCAL Workshop on Learning Methods for Text Understanding and Mining, 2004.
Theme Topic Mixture Model: A Graphical Model for Document Representation [link]Paper  abstract   bibtex   
Automatic Text Processing tasks, 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 review of a family of document density estimation models for representing documents. Inside this family we derive another possible model: 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. An experiment reports the performance of the different models in this family over a common task.
@inproceedings{keller:2004:pascal,
  author = {M. Keller and S. Bengio},
  title = {Theme Topic Mixture Model: A Graphical Model for Document Representation},
  booktitle = {{PASCAL} Workshop on Learning Methods for Text Understanding and Mining},
  year = 2004,
  url = {publications/ps/keller_2004_pascal.ps.gz},
  pdf = {publications/pdf/keller_2004_pascal.pdf},
  djvu = {publications/djvu/keller_2004_pascal.djvu},
  original = {2004/ttmm_jair},
  web = {http://eprints.pascal-network.org/archive/00000028/},
  topics = {ranking,graphical_models},
  abstract = {Automatic Text Processing tasks, 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 review of a family of document density estimation models for representing documents. Inside this family we derive another possible model: 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. An experiment reports the performance of the different models in this family over a common task.},
  categorie = {C},
}
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