Factorial LDA: Sparse Multi-Dimensional Text Models

@inproceedings{Paul2012FactorialLS,
  title={Factorial LDA: Sparse Multi-Dimensional Text Models},
  author={Michael J. Paul and Mark Dredze},
  booktitle={NIPS},
  year={2012}
}
Latent variable models can be enriched with a multi-dimensional structure to consider the many latent factors in a text corpus, such as topic, author perspective and sentiment. We introduce factorial LDA, a multi-dimensional model in which a document is influenced by K different factors, and each word token depends on a K-dimensional vector of latent variables. Our model incorporates structured word priors and learns a sparse product of factors. Experiments on research abstracts show that our… CONTINUE READING
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