Advanced Topics in Markov Models and POS Tagging

1. Markov Model Variants

• Hidden Markov Models (HMMs): Learn how HMMs are used for sequence labeling tasks like POS tagging.
• Higher-Order Markov Models: Explore trigram and n-gram models for capturing longer dependencies.
• Conditional Random Fields (CRFs): Study CRFs as an extension for structured prediction.

2. Sequence Labeling in NLP

• Compare HMMs, CRFs, and neural sequence models for POS tagging.
• Study applications in named entity recognition and chunking.
• Analyze challenges in tagging ambiguous and rare words.

3. Computational Implementation

• Algorithms for training and decoding (Viterbi, Forward-Backward).
• Efficient storage and computation for large tagsets.
• Handling data sparsity and smoothing techniques.

4. Research Papers

1. "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition" (Rabiner, 1989)
2. "Part-of-Speech Tagging Using Markov Models" (Church, 1988)
3. "Bidirectional LSTM-CRF Models for Sequence Tagging" (Huang et al., 2015)

5. Online Resources

1. Video Lectures

   • Stanford CS224N: Sequence Models and HMMs
   • NPTEL: Hidden Markov Models in NLP
   • Coursera: Sequence Models in NLP

2. Interactive Tools

   • Online HMM POS Taggers
   • Sequence labeling visualizers
   • Markov chain simulators

3. Code Repositories

   • Open-source HMM implementations (Python, Java)
   • Sequence labeling datasets
   • Tutorials for building POS taggers

6. Practical Exercises

1. Basic Exercises

   • Implement a simple HMM POS tagger
   • Calculate emission and transition probabilities
   • Visualize state transitions in Markov chains

2. Advanced Projects

   • Build a domain-adapted POS tagger
   • Compare HMMs with neural sequence models
   • Analyze tagging errors and confusion matrices

3. Research Projects

   • Study the impact of smoothing on tagging accuracy
   • Explore multilingual POS tagging with HMMs
   • Integrate morphological features into Markov models

7. Further Reading

Books

1. "Speech and Language Processing" by Jurafsky & Martin (Chapters on HMMs and POS Tagging)
2. "Foundations of Statistical Natural Language Processing" by Manning & Schütze
3. "Pattern Recognition and Machine Learning" by Bishop (Markov models section)

Journals

1. Computational Linguistics
2. Natural Language Engineering
3. Journal of Machine Learning Research