After completing this experiment, students will be able to:

1. Understand POS Tagging Fundamentals: Define Part-of-Speech tagging and explain its significance in Natural Language Processing, identifying different grammatical categories (noun, verb, adjective, adverb, etc.) and their linguistic functions with 85% accuracy.

2. Compare Tagging Algorithms: Analyze and differentiate between various POS tagging algorithms including Hidden Markov Models (HMM) and Conditional Random Fields (CRF), understanding their computational approaches and performance characteristics.

3. Evaluate Feature Impact: Assess the role of context features (unigram, bigram, trigram) in improving tagging accuracy, and analyze how training corpus size affects model performance through hands-on experimentation.

4. Apply Interactive Analysis: Demonstrate proficiency in using the interactive simulation to explore different algorithm configurations, interpret performance metrics (accuracy, precision, recall), and understand their significance in model evaluation.

5. Analyze Cross-linguistic Patterns: Compare POS tagging challenges and patterns between English and Hindi, understanding how linguistic ambiguity and morphological complexity affect automated tagging systems.

Learning Focus

• Master fundamental concepts of Part-of-Speech tagging in NLP
• Compare statistical and rule-based approaches to POS tagging
• Experiment with algorithm parameters and observe accuracy effects
• Interpret performance metrics and their practical significance
• Apply theoretical knowledge to real text analysis scenarios
• Understand the foundational role of POS tagging in advanced NLP tasks