Theme Topic Mixture Model: A Graphical Model for Document Representation
| Type of publication: | Conference paper |
| Citation: | keller:pascal-wshp:2004 |
| 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. |
| Userfields: | ipdmembership={learning}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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