Follow these steps to complete the Viterbi Decoding experiment:

Step 1: Corpus Selection

• Open the simulation interface
• Select a corpus from the dropdown menu (Corpus A, B, or C)
• By default, Corpus A is loaded with the training sentence: "Book a car. Park the car. The book is in the car. The car is in a park."
• Observe how this training corpus is used to generate probability matrices

Step 2: Understanding the Training Data

• Left Pane: Examine the full training sentence and the derived probability matrices
• Emission Matrix: Shows P(word|tag) - probability of each word given each POS tag
• Transition Matrix: Shows P(tag₂|tag₁) - probability of tag transitions
• Note how these matrices capture statistical patterns from the training corpus

Step 3: Analyze the Test Sentence

• Right Pane: Focus on the test sentence (e.g., "Book a park" for Corpus A)
• Click the info icon (ⓘ) next to the test sentence to understand why this specific sentence was chosen
• Observe the empty Viterbi decoding table with:
  • Columns representing words in the test sentence
  • Rows representing possible POS tags (Noun, Verb, Det)

Step 4: Fill the Viterbi Table

• Start with the first column (first word)
• For each cell, calculate: emission probability × transition probability
• Work column by column from left to right
• For subsequent columns, use: max(previous_column × transition) × emission
• Enter your calculated values in the input fields

Step 5: Validate Your Work

• Click "Check" to validate all your entries
• The system provides immediate feedback:
  • ✅ Correct values are accepted
  • ❌ Incorrect values trigger error messages
• Revise incorrect entries and check again

Step 6: Use Learning Aids

• "Show Hint": Click for algorithmic guidance and computation tips
• "Show Answer": Compare your entries with correct values (only after attempting)
  • Red values indicate your incorrect entries
  • Green values show the correct answers
  • Side-by-side comparison helps identify calculation errors

Step 7: Complete the Decoding

• Once all Viterbi table entries are correct, the simulation automatically reveals:
  • The optimal POS tag sequence for the test sentence
  • A results table showing the decoded tags below each word

Step 8: Try Different Corpora

• Select Corpus B: "The quick brown fox jumps over the lazy dog" → "The quick fox jumps"
• Select Corpus C: "She sells sea shells by the sea shore" → "She sells shells"
• Compare how different training data affects:
  • Probability matrix values
  • Optimal tag sequences
  • Decoding difficulty

Step 9: Reset and Practice

• Use "Reset" to clear your work and start over
• Try different corpora to practice with various vocabulary and sentence structures
• Focus on understanding the relationship between training data and decoding outcomes

Learning Tips

• Mathematical Understanding: Focus on how each cell value is computed using dynamic programming
• Linguistic Intuition: Consider why certain tag sequences are more probable than others
• Error Analysis: When answers are incorrect, analyze whether the error was in:
  • Emission probability lookup
  • Transition probability lookup
  • Mathematical computation
  • Understanding of the algorithm

This hands-on approach reinforces theoretical understanding of the Viterbi algorithm while providing practical experience with statistical POS tagging.