part of speech tagger on brown corpus data set

Implement a part-of-speech tagger in Python, using Bayesian networks.

The program implements part of speech tagger using following algorithms and compares their accuracy for each sentence:

1. Naive inference
2. Sampling (Gibbs)
3. Approximate max-marginal inference
4. Exact maximum a posteriori inference (Viterbi)
5. Voting based approach

Commandline: python label.py train_file test_file