Statistical, ML and Neural Models of POS Tagging #
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Learning Objectives #
- Explain the difference between likelihood computation and sequence decoding.
- Apply the Forward and Viterbi algorithms conceptually to sequence models.
- Describe the strengths and limitations of MEMMs.
- Explain why bidirectional and neural models improve POS tagging.
Chapter Map #
| Section | Topic | Status |
|---|---|---|
| 1 | Likelihood Computation: The Forward Algorithm | ☐ |
| 2 | Decoding: The Viterbi Algorithm | ☐ |
| 3 | Maximum Entropy Markov Model | ☐ |
| 4 | Bidirectionality | ☐ |
| 5 | Neural Network Models for POS | ☐ |
Big Picture #
1. Likelihood Computation: The Forward Algorithm ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
2. Decoding: The Viterbi Algorithm ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
3. Maximum Entropy Markov Model ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
4. Bidirectionality ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
5. Neural Network Models for POS ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
Practical Exploration #
Compare statistical and neural POS taggers on a small annotated sample.
# Add a minimal, well-commented Python example here.
Comparison Table #
| Concept or Model | Main Idea | Strength | Limitation | Typical Use |
|---|---|---|---|---|
Common Mistakes #
Practice Questions #
Key Takeaways #
Understanding Checklist #
- I can explain Likelihood Computation: The Forward Algorithm without referring to notes.
- I can explain Decoding: The Viterbi Algorithm without referring to notes.
- I can explain Maximum Entropy Markov Model without referring to notes.
- I can explain Bidirectionality without referring to notes.
- I can explain Neural Network Models for POS without referring to notes.