%Aigaion2 BibTeX export from Idiap Publications
%Friday 20 December 2024 09:40:51 PM

@INPROCEEDINGS{keller:pascal-wshp:2004,
         author = {Keller, Mikaela and Bengio, Samy},
       projects = {Idiap},
          title = {Theme {T}opic {M}ixture {M}odel: A Graphical Model for Document Representation},
      booktitle = {Pascal Workshop on Text Mining and Understanding},
           year = {2004},
           note = {IDIAP-RR 04-05},
       crossref = {keller:rr04-05},
       abstract = {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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2004/keller-pascal-wshp-2004.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/papers/2004/keller-pascal-wshp-2004.ps.gz},
ipdmembership={learning},
}



crossreferenced publications: 
@TECHREPORT{keller:rr04-05,
         author = {Keller, Mikaela and Bengio, Samy},
       projects = {Idiap},
          title = {Theme {T}opic {M}ixture {M}odel: A Graphical Model for Document Representation},
           type = {Idiap-RR},
         number = {Idiap-RR-05-2004},
           year = {2004},
    institution = {IDIAP},
           note = {Published in PASCAL Workshop on Text Mining and Understanding, january 2004},
       abstract = {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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2004/keller-idiap-rr-04-05.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2004/keller-idiap-rr-04-05.ps.gz},
ipdmembership={learning},
}