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Computational Linguistics Syllabus |
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Course Outline |
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Linguistics 581 |
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Week |
Day |
Reading |
Assignment |
Lecture |
Background |
Code |
| Week 0 | Wed Jan 22 | Chapter 1 of Jurafsky and Martin (J&M) History of Computational Linguistics. Chapter 2 J&M [Blackboard], Finite-State Automata, Regular Expressions Read BOTH Chapter 2 and the extra file labeled "Finite-State Automata" | Assignment 1, Regular expression Python demo script. | Textbook Ch. 2 slides Non-deterministic automata, epsilon transitions. [Also see blackboard slides] | ||
| Week 1 | Wed Jan 29 | Chapter 4: J&M. Language modeling (Ngram models). Word counting, frequency dictionaries, simple ngram models, the training corpus. Introduction to NLTK. | Python releases, Demo Script for computing ngram counts, The Pollard assignment and smoothing assignment are due Feb. 11. | Ngrams, Brief probability intro, Python & unicode. | Peter Norvig on ngrams. | |
| Week 2 | Wed Feb 05 | Chapter 4: Section 4.1-4.5 of chapter 4. Practicalities. Sections 4.5.3 Chapter 4. Sections 4.4 and 4.5.1, 4.5.2 Chapter 4. Smoothing, Add-1 smoothing, Kneser-Ney smoothing. Unknown words. | Entropy and Cross-Entropy. (Entropy as expected information value; cross-entropy as an evaluation tool) Smoothing Lecture. Kneser-Ney Lecture. | |||
| Week 3 | Wed Feb 12 | Chapter 4: Section 4.5 Kneser-Ney smoothing. | ||||
| Week 4 | Wed Feb 19 | Chapter 5: J&M. [Blackboard] Spelling Correction. Noisy Channel. Generative models. | Noisy channel assignment zip file (contains Python notebook). Due Feb. 26. Regular expressions assignment solution script (Python). | |||
| Week 5 | Wed Feb 26 | Chapter 6: J&M. [Blackboard] Naive Bayes Classification. More generative models. Chapter 7. [Blackboard] Logistic Regression | Naive Bayes assignment (Due Mar. 4). | Naive Bayes lecture. | ||
| Week 6 | Wed Mar 04 | Chapter 7. [Blackboard] Logistic Regression | Logistic Regression assignment (Due Mar. 11). | |||
| Week 7 | Wed Mar 11 | Chapter 9: J&M. [Blackboard] Noisy channel model redux, Sequence models. HMMs. | Intro to HMMs, Forward/Backward Algorithm on Jason Eisner's ice cream HMM. | |||
| Week 8 | Wed Mar 18 | Chapter 10: J&M [Blackboard] Word-class and part of speech tagging. Rule-based taggers, decoding with HMMs. | Viterbi assignment (Hardcopy due Mar. 18 or Mar. 25). Practice Viterbi Problem, Practice Problem solution, Practice problem template. | Lecture. Tagging slides HMM Taggers/HMM models. | ||
| Week 9 | Wed Mar 25 | Brown tagging NB, Naive Bayes solution, Noisy channel assignment solution, Viterbi assignment part one solution, Viterbi assignment solution, Viterbi assignment calculations, Midterm 2020! | Lecture: Review | |||
| Week 10 | Wed Apr 01 | H'day | H'day | H'day | H'day | H'day |
| Week 11 | Wed Apr 08 | Chapter 11. J&M Context Free Grammars of English, Treebanks, [Jurafsky and Martin 3rd Ed, Blackboard] | Grammar assignment (Due Apr. 15). | |||
| Week 12 | Wed Apr 15 | Chapter 12 Parsing. [Jurafsky and Martin 3rd Ed, Blackboard] CKY algorithm (bottum up parsing with a chart), | Parsing assignment (CKY) (Due Tues., April 22, Note: Part C, using probabilities is Due Tu, Apr 29. Solution for Grammar assignment. | Jurafsky/ Martin Grammar chapter lecture (see blackboard), Tree structure: Main ideas. Jurafsky Martin parsing lecture (see blackboard). | td_parser-0.1:Python implementation of a recursive descent top down recognizer. | |
| Week 13 | Wed Apr 22 | Chapter 12 Probabilistic Parsing. [Jurafsky and Martin 3rd Ed, Blackboard] probabilistic extension of CKY. Shift-reduce Dependency parsing, Deep learning parsers (RNNs) | Parsing assignment (CKY) (Part C: Probabilities, Due Wed., May 6), Solution for Grammar assignment, CKY assignment solution | |||
| Week 14 | Wed Apr 29 | Chapter 15. Vector Semantics [Jurafsky and Martin 3rd Ed, Blackboard] | Solution for Parts A and B, Prob parsing assignment notes, Parsing assignment (CKY) (Part C: Probabilities, Due Wed., May 6), Word Vector Assignment, due Wed. May 6, | |||
| Week 15 | Wed May 06 | Chapter 16. Word Embeddings [Jurafsky and Martin 3rd Ed, Blackboard] | 2020 Compling final, Solution for Word Vector problem, Word vector calculation example. Word Vector practice NB, Solution to parsing problem, Part C. Solution for Parts A and B, Prob parsing assignment notes, | Last class day | ||
| Week 16 | Wed May 13 | Chapter 8. Neural Nets [Jurafsky and Martin 3rd Ed, Blackboard] | Quiz on Vector Semantics & Neural Nets. | |||
| Week 17 | Wed May 20 | |||||
| Week 18 | Wed May 27 | Chapter 8, 12-13, 15, 16. Review | ||||
| Week 19 | Wed Jun 03 | 2019 Final, | ||||
| Week 20 | Wed Jun 10 | |||||
| Week 21 | Wed Jun 17 | |||||
| Week 22 | Wed Jun 24 |
