Step-by-Step Procedure for Building a Chunker

Step 1: Select Language

• Choose the target language (English or Hindi) for chunking analysis.

Step 2: Choose Training Corpus Size

• Select the size of the training data. Larger corpora may improve accuracy but take longer to train.

Step 3: Select Algorithm

• Pick the machine learning model for chunking: Hidden Markov Model (HMM) or Conditional Random Field (CRF).

Step 4: Choose Feature Set

• Decide which features to use for training:
  • Lexicon only
  • POS tags only
  • Lexicon + POS tags (recommended for best results)

Step 5: Train and Evaluate

• Click "Check Accuracy" to train the chunker and view its accuracy for your chosen configuration.
• Review example sentences with predicted chunk boundaries.

Step 6: Experiment and Compare

• Try different combinations of features, corpus sizes, and algorithms.
• Use "Reset / Try Another Configuration" to start over and explore more settings.

Tips:

• Use larger corpora and richer feature sets for higher accuracy.
• Compare HMM and CRF results to understand model differences.
• Analyze error patterns in the output to improve your chunker.

OUTPUT:

• The accuracy of the chunker for the selected configuration is shown.
• Example sentences with their predicted chunks are displayed for better understanding.
• You can use the "Reset / Try Another Configuration" button to start over and explore different settings.