Gibbs Sampling and Variational Inference for HDP-HMM
PoS tagging is essential for NLP tasks, yet supervised methods depend heavily on annotated data. Unsupervised methods like Hierarchical Dirichlet Process Hidden Markov Models (HDP-HMM) offer a promising alternative. This study provides a detailed derivation of a Gibbs sampling and a variational inference algorithm for HDP-HMM and implements the code.