Textbooks

1. Speech and Language Processing - An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition (3rd Edition)
BY: Daniel Jurafsky and James H. Martin - Chapter 8: Part-of-Speech Tagging & Appendix A: Hidden Markov Models
Comprehensive coverage of HMMs, Viterbi algorithm, and POS tagging with mathematical foundations.

2. Foundations of Statistical Natural Language Processing
BY: Christopher D. Manning and Hinrich Schütze - Chapter 10: Part-of-Speech Tagging
Detailed statistical approaches to sequence labeling and probabilistic models.

3. Natural Language Processing with Python
BY: Steven Bird, Ewan Klein, and Edward Loper - Chapter 5: Categorizing and Tagging Words
Practical implementation of POS tagging algorithms with NLTK library.

4. Introduction to Algorithms (4th Edition)
BY: Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein - Chapter 15: Dynamic Programming
Mathematical foundations of dynamic programming used in Viterbi algorithm.

Video Lectures and Online Courses

NPTEL Courses:

• Natural Language Processing - IIT Bombay (Lectures 15-18)
• Artificial Intelligence - IIT Madras (HMM Lectures)

Stanford CS224N:

• Natural Language Processing with Deep Learning - Lecture 6: Language Models and RNNs
• Dan Jurafsky's NLP Lectures - HMM and Viterbi Algorithm

MIT OpenCourseWare:

• Artificial Intelligence - Probabilistic Inference and HMMs
• Introduction to Machine Learning - Sequence Models

YouTube EDU Channels:

• 3Blue1Brown - Dynamic Programming Visualization
• Computerphile - Hidden Markov Models Explained

Research Papers and Articles

1. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition
BY: Lawrence R. Rabiner
Proceedings of the IEEE, Vol. 77, No. 2, 1989

2. Error-Driven Learning and Natural Language Processing: A General Framework
BY: Eric Brill
Computational Linguistics, Vol. 21, No. 4, 1995

3. Part-of-Speech Tagging Using a Variable Memory Markov Model
BY: Hinrich Schütze and Yoram Singer
Proceedings of ACL, 1994

Online Resources and Tools

Academic Resources:

• ACL Anthology - Research papers in computational linguistics
• arXiv: Computation and Language - Latest research in NLP
• Stanford NLP Group - Tools and resources

Interactive Tools:

• NLTK POS Tagger - Python implementation
• spaCy POS Tagging - Industrial-strength tagger
• Penn Treebank Tagset - Standard POS tags

Algorithm Visualizations:

• Algorithm Visualizer - Dynamic programming demonstrations
• VisuAlgo - Interactive DP algorithm visualization

Additional Reading

For Advanced Study:

• "Pattern Recognition and Machine Learning" by Christopher M. Bishop - Chapter 13: Sequential Data
• "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
• "Probabilistic Graphical Models" by Daphne Koller and Nir Friedman

For Implementation:

• "Natural Language Processing in Action" by Hobson Lane, Cole Howard, and Hannes Hapke
• "Programming Collective Intelligence" by Toby Segaran
• "Think Stats" by Allen B. Downey - Statistical thinking for programmers

Practice Resources

Datasets and Corpora:

• Penn Treebank - Standard POS tagging dataset
• Universal Dependencies - Multi-language POS annotation
• Brown Corpus - Classic tagged corpus

Programming Challenges:

• Kaggle NLP Competitions - Real-world applications
• HackerRank AI Domain - Algorithm practice
• LeetCode Dynamic Programming - DP problem practice

Assessment Tools:

• CoNLL Shared Tasks - International evaluation campaigns
• SemEval Tasks - Semantic evaluation exercises