Hierarchical Latent Dirichlet Allocation

Note: this repository should only be used for education purpose. For production use, I'd recommend using https://github.com/bab2min/tomotopy which is more production-ready

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Hierarchical Latent Dirichlet Allocation (hLDA) addresses the problem of learning topic hierarchies from data. The model relies on a non-parametric prior called the nested Chinese restaurant process, which allows for arbitrarily large branching factors and readily accommodates growing data collections. The hLDA model combines this prior with a likelihood that is based on a hierarchical variant of latent Dirichlet allocation.

Hierarchical Topic Models and the Nested Chinese Restaurant Process

The Nested Chinese Restaurant Process and Bayesian Nonparametric Inference of Topic Hierarchies

Implementation

• hlda/sampler.py is the Gibbs sampler for hLDA inference, based on the implementation from Mallet having a fixed depth on the nCRP tree.

Installation

• Simply use pip install hlda to install the package.
• An example notebook that infers the hierarchical topics on the BBC Insight corpus can be found in notebooks/bbc_test.ipynb.