After completing this experiment, students will be able to:

1. Understand Viterbi Algorithm Mechanics: Comprehend the step-by-step process of the Viterbi algorithm as a dynamic programming solution for finding the most probable sequence of hidden states in Hidden Markov Models with 90% accuracy.

2. Apply Mathematical Foundations: Correctly compute emission probabilities P(word|tag) and transition probabilities P(tag_j|tag_i) from training corpora, and use these matrices to fill Viterbi tables systematically for test sentences.

3. Implement Dynamic Programming Logic: Demonstrate proficiency in the recursive computation of maximum probabilities at each step, maintaining backpointers for optimal path recovery, and understanding the O(N×T²) time complexity advantage over brute force O(T^N) approaches.

4. Analyze POS Tagging Process: Interpret how the algorithm balances emission probabilities (word-tag likelihood) with transition probabilities (tag sequence likelihood) to achieve optimal linguistic accuracy in part-of-speech assignment.

5. Practice Interactive Problem-Solving: Develop hands-on skills using the simulation to decode POS tag sequences across different corpus examples, comparing how training data characteristics affect probability distributions and decoding outcomes.

6. Evaluate Algorithm Efficiency: Understand the computational advantages of dynamic programming in sequence labeling tasks and recognize applications beyond POS tagging including speech recognition, bioinformatics, and named entity recognition.

Learning Focus

• Master the Viterbi decoding process through interactive matrix filling
• Understand probability computation from emission and transition matrices
• Apply dynamic programming principles to sequence labeling problems
• Analyze the relationship between training data and model performance
• Compare different corpus examples and their impact on tagging accuracy