Efficient Methods for Inferring Large Sparse Topic Hierarchies

@inproceedings{Downey2015EfficientMF,
  title={Efficient Methods for Inferring Large Sparse Topic Hierarchies},
  author={Doug Downey and Chandra Bhagavatula and Yi Yang},
  booktitle={ACL},
  year={2015}
}
Latent variable topic models such as Latent Dirichlet Allocation (LDA) can discover topics from text in an unsupervised fashion. However, scaling the models up to the many distinct topics exhibited in modern corpora is challenging. “Flat” topic models like LDA have difficulty modeling sparsely expressed topics, and richer hierarchical models become computationally intractable as the number of topics increases. In this paper, we introduce efficient methods for inferring large topic hierarchies… Expand
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