This interactive simulation allows you to explore Part-of-Speech (POS) tagging by configuring different parameters and observing their impact on tagging accuracy. Follow these Steps to conduct your experiments.

Step 1: Access the Simulation

• Open the simulation interface
• Read the instructions panel to understand the overall workflow
• Click on the instructions header to expand/collapse detailed guidance

Step 2: Language Selection

• Click on the language dropdown menu
• Choose from available options:
  • English: Standard Latin script with rich morphology
  • Hindi: Devanagari script with complex morphological features
• Note: Different languages present unique tagging challenges due to:
  • Script differences (Latin vs. Devanagari)
  • Morphological complexity
  • Word order variations

Step 3: Configure Training Corpus Size

• Select the size of the training corpus from the dropdown:
  • Small (1K sentences): Fast training, limited accuracy
  • Medium (10K sentences): Balanced performance
  • Large (50K sentences): Best accuracy, slower training
  • Extra Large (100K sentences): Maximum accuracy potential

Impact: Larger corpora provide:

• More diverse word-tag combinations
• Better statistical estimates
• Improved handling of rare constructions
• Higher computational requirements

Step 4: Algorithm Selection

• Choose the machine learning algorithm:
  • HMM (Hidden Markov Model):
    • Probabilistic approach
    • Uses transition and emission probabilities
    • Efficient with moderate accuracy
  • CRF (Conditional Random Field):
    • Discriminative model
    • Handles rich feature sets
    • Higher accuracy, more computational cost

Step 5: Feature Configuration

• Select the context features for training:
  • Unigram: Uses only current word
    • Fastest processing
    • Limited context information
  • Bigram: Considers current and previous word/tag
    • Better disambiguation
    • Moderate computational cost
  • Trigram: Uses current and two previous words/tags
    • Rich contextual information
    • Higher accuracy for complex constructions
    • Increased computational requirements

Step 6: Train and Test the Model

• Click the "Train & Test" button
• Wait for processing: The system will:
  • Simulate training with your selected parameters
  • Calculate accuracy metrics
  • Prepare demo examples

Step 7: Analyze Results

The results panel will display:

Accuracy Metrics

• Overall Accuracy: Percentage of correctly tagged words
• Performance Summary: Brief analysis of results
• Configuration Details: Reminder of selected parameters

Interactive Demo

• Example Dropdown: Select from pre-processed sentences
• POS Tag Visualization: See tagged output with:
  • Original sentence
  • Word-by-word POS tags
  • Color-coded visualization (if available)

Step 8: Experiment with Different Configurations

• Click "Try Another Configuration" to reset the simulation
• Systematic Experimentation:
  1. Keep some parameters constant while varying others
  2. Compare results across different configurations
  3. Note patterns and performance trends