After completing this experiment, students will be able to:

1. Understand HMM Fundamentals: Define Hidden Markov Models and explain their application to POS tagging, identifying the role of hidden states, observations, and probability matrices with 85% accuracy.

2. Calculate Probability Matrices: Compute transition and emission probabilities from annotated training corpora, demonstrating understanding of how statistical patterns are extracted from linguistic data.

3. Apply the Viterbi Algorithm: Implement and trace through the Viterbi algorithm to find optimal POS tag sequences, understanding dynamic programming principles in sequence labeling tasks.

4. Analyze Contextual Disambiguation: Evaluate how HMMs resolve word ambiguity through contextual probabilities, comparing different tag sequences and their likelihood scores.

5. Interpret Model Performance: Assess the impact of training data size and probability values on tagging accuracy, understanding the relationship between data quality and model effectiveness.

Learning Focus

• Master probabilistic sequence modeling with HMMs
• Calculate and interpret transition and emission matrices
• Apply the Viterbi algorithm for optimal sequence prediction
• Understand how context resolves linguistic ambiguity
• Analyze the relationship between training data and model performance
• Connect theoretical concepts to practical NLP applications