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EMNLP
2007
9 years 3 months ago
Bayesian Document Generative Model with Explicit Multiple Topics
In this paper, we proposed a novel probabilistic generative model to deal with explicit multiple-topic documents: Parametric Dirichlet Mixture Model(PDMM). PDMM is an expansion of...
Issei Sato, Hiroshi Nakagawa
NIPS
2001
9 years 3 months ago
Latent Dirichlet Allocation
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian m...
David M. Blei, Andrew Y. Ng, Michael I. Jordan
AAAI
2010
9 years 3 months ago
A Two-Dimensional Topic-Aspect Model for Discovering Multi-Faceted Topics
This paper presents the Topic-Aspect Model (TAM), a Bayesian mixture model which jointly discovers topics and aspects. We broadly define an aspect of a document as a characteristi...
Michael Paul, Roxana Girju
CVPR
2009
IEEE
9 years 5 months ago
Robust unsupervised segmentation of degraded document images with topic models
Segmentation of document images remains a challenging vision problem. Although document images have a structured layout, capturing enough of it for segmentation can be dif´Čücult....
Timothy J. Burns, Jason J. Corso
ICML
2010
IEEE
9 years 2 months ago
Spherical Topic Models
We introduce the Spherical Admixture Model (SAM), a Bayesian topic model for arbitrary 2 normalized data. SAM maintains the same hierarchical structure as Latent Dirichlet Allocat...
Joseph Reisinger, Austin Waters, Bryan Silverthorn...
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