Hidden Markov Model

I built a natural language processing algorithm using a Hidden Markov Model. It labels each word in a sentence with its part of speech, based off of the book "Natural Language Processing with Python." Here are some of the technical details of this project:

1. Data Collection: Utilized the Brown corpus for training and testing to ensure a diverse representation of sentence structures.

2. Model Training: Implemented an HMM to calculate transition and observation probabilities, converting counts to log probabilities for accuracy.

3. Viterbi Algorithm: Optimally tagged words in sequence, and improved tagging accuracy through dynamic programming.