This experiment demonstrates Hidden Markov Models for POS tagging through an interactive simulation. Follow these steps to understand and practice HMM-based POS tagging:

Step 1: Understanding the Interface

The simulation interface consists of several key components:

• Corpus Display Area: Shows the training corpus with word/tag pairs
• Transition Matrix: Displays probabilities of moving from one POS tag to another
• Emission Matrix: Shows probabilities of observing words given POS tags
• Input Section: Where you can enter sentences for POS tagging
• Results Display: Shows the step-by-step Viterbi algorithm execution

Step 2: Examine the Training Corpus

1. Review the provided training corpus displayed in the interface
2. Observe how words are paired with their corresponding POS tags
3. Notice the format: word/tag for each token
4. Pay attention to special markers like EOS/eos (End of Sentence)

Step 3: Analyze the Probability Matrices

Transition Matrix:

1. Examine the transition probabilities between POS tags
2. Each cell shows P(tag_j | tag_i) - probability of tag_j following tag_i
3. Notice how certain tag sequences are more likely than others
4. Observe the probabilities for sentence-initial tags from EOS

Emission Matrix:

1. Study the emission probabilities for words given POS tags
2. Each cell shows P(word | tag) - probability of observing a word given a tag
3. Notice how some words can have multiple possible tags with different probabilities
4. Observe cases of ambiguous words (like "cut" which can be noun or verb)

Step 4: Interactive POS Tagging

1. Enter a Test Sentence: Input a sentence in the provided text field
2. Initiate Tagging: Click the "Tag Sentence" or similar button to start the process
3. Observe the Viterbi Algorithm: Watch the step-by-step execution:
   • Initialization: See how initial probabilities are calculated for the first word
   • Forward Pass: Observe how probabilities are computed for each subsequent word
   • Path Tracking: Notice how the algorithm keeps track of the most likely paths
   • Backtracking: See how the final tag sequence is determined

Step 5: Analyze Results

1. Review the Final Tag Sequence: Examine the most likely POS tags assigned to each word
2. Study the Probability Scores: Understand the confidence scores for each tagging decision
3. Compare Different Paths: If available, compare alternative tag sequences and their probabilities
4. Understand Context Effects: Notice how context influences tag assignment for ambiguous words

Step 6: Experiment with Different Inputs

1. Try Various Sentence Types:

   • Simple sentences with common words
   • Sentences with ambiguous words
   • Complex sentences with multiple clauses
   • Sentences with less common words

2. Observe Different Behaviors:

   • How does sentence length affect processing?
   • What happens with unknown or rare words?
   • How do different word orders impact tagging?

Step 7: Matrix Calculation Practice (if available)

If the simulation includes interactive matrix calculation:

1. Fill Emission Probabilities:

   • Count word-tag co-occurrences in the corpus
   • Calculate P(word|tag) = count(word,tag) / count(tag)
   • Enter calculated values in the matrix cells

2. Fill Transition Probabilities:

   • Count tag-tag transitions in the corpus
   • Calculate P(tag_j|tag_i) = count(tag_i,tag_j) / count(tag_i)
   • Enter calculated values in the matrix cells

3. Verify Your Calculations: Use the "Check" button to validate your answers

   • Correct answers will be highlighted in green
   • Incorrect answers will be highlighted in red
   • Review and correct any mistakes

Step 8: Advanced Exploration

1. Modify Parameters (if available): Experiment with different probability values to see their effects
2. Compare Algorithms: If multiple algorithms are available, compare their performance
3. Error Analysis: Identify common tagging errors and understand their causes
4. Performance Evaluation: Analyze accuracy and efficiency metrics

Learning Outcomes

By completing this procedure, you will:

• Understand how HMMs work for POS tagging
• Learn to calculate transition and emission probabilities
• Experience the Viterbi algorithm in action
• Recognize the importance of context in disambiguation
• Appreciate the challenges and limitations of statistical NLP approaches

Tips for Success

• Take Time to Understand: Don't rush through the matrices; understanding the probabilities is crucial
• Experiment Actively: Try different sentences to see how the algorithm responds
• Pay Attention to Ambiguity: Focus on how ambiguous words are resolved
• Connect Theory to Practice: Relate what you see in the simulation to the theoretical concepts
• Ask Questions: Consider why certain tagging decisions are made and explore edge cases