This schedule is tentative and subject to change as the semester progresses.
| Date | Lecture | Reading | Assignment | ||
|---|---|---|---|---|---|
| Introduction | |||||
| W | 1/10 | 1 | What is NLP? | SLP2 Ch. 1 | |
| M | 1/15 | No class or office hours: MLK Day | |||
| W | 1/17 | 2 | Why do we need data?; Basic Text Processing (Regular Expressions, Normalization) | Working with text in Python 3, SLP3 2.0–2.3, NLTK Book 3.2–3.10 | A1: Text Processing for "The Chaos" |
| M | 1/22 | 3 | Overview of Linguistics | SLP2 1.1, 25.1 | |
| W | 1/24 | 4 | Working with Unix: text processing; version control with git | Unix Text Commands, Git Commands | |
| F | 1/26 | A1 due | |||
| N-grams | |||||
| M | 1/29 | 5 | N-gram language models | SLP3 4.0–4.4; Goldwater probability review through section 4 | |
| W | 1/31 | 6 | Language models: more smoothing | A2: Language Models | |
| Classification | |||||
| M | 2/5 | 7 | Classification: naïve Bayes | SLP3 6.0–6.5 | |
| W | 2/7 | 8 | Lexical semantics: senses, relations, classes; Linear models for classification: features & weights | SLP3 17.0–17.4, 7.1 | |
| F | 2/9 | A2 due | |||
| M | 2/12 | 9 | Linear models for classification: discriminative learning (perceptron, SVMs, MaxEnt) | Daumé The Perceptron: 4.0–4.3; SLP3 6.6–6.8, ch. 7 (Further readings are suggested in slides) | A3: Perceptron |
| Sequential Prediction | |||||
| W | 2/14 | 10 | Parts of speech | SLP3 10.0–10.3; Eisenstein Notes, 7.1: Part-of-speech tagging | |
| M | 2/19 | No class or office hours: Presidents' Day | |||
| W | 2/21 | 11 | POS tagging: HMMs | SLP3 9.0–9.2 | A3 due |
| M | 2/26 | 12 | Algorithms for HMMs (mainly Viterbi) | SLP3 9.4, 10.4 | |
| W | 2/28 | 13 | MIDTERM EXAM | Study guide | |
| F | 3/2 | A4: HMM | |||
| M-F | 3/5-9 | No class or office hours: Spring Break | |||
| M | 3/12 | 14 | Annotation; Universal POS annotation activity (tagset) | ||
| W | 3/14 | 15 | Annotation activity contd.; Discriminative tagging with the structured perceptron | Eisenstein Notes, 6.5 (no need to read beyond structured perceptron); Neubig slides | |
| Hierarchical Sentence Structure | |||||
| M | 3/19 | 16 | English syntax, CFGs | SLP3 11.0–11.2, skim 11.3 | A4 due |
| W | 3/21 | 🌨❄⛄ | |||
| F | 3/23 | P0: Suggest possible topics, form project teams | |||
| M | 3/26 | 17 | (P)CFG parsing | SLP3 12.0–12.2, 13.0–13.3 | A5: Syntax |
| W | 3/28 | 18 | (P)CFG parsing contd.; forming project teams | ||
| F | 3/30 | P1: 1-2 page proposal due | |||
| M | 4/2 | No class or office hours: Easter Break | |||
| W | 4/4 | 19 | Dependency parsing (includes bonus slides not covered in class) | SLP3 14.0–14.4.1, 14.6 | |
| W-Th | 4/4-5 | P2: groups meet with instructor & TA | |||
| Other Learning Paradigms and Applications | |||||
| M | 4/9 | 20 | Coreference resolution (guest lecture: Amir Zeldes) | A5 due | |
| W | 4/11 | 21 | Semantic role labeling | SLP3 ch. 22, pp. 1–10 | P3: Progress update, including lit review, due |
| M | 4/16 | 22 | Machine translation | ||
| W | 4/18 | 23 | Distributional representations and similarity (Sean) | SLP3 Ch. 15, 16.0–16.1, 16.4 | |
| M | 4/23 | 24 | Deep learning and neural networks (Austin) | ||
| W | 4/25 | 25 | PROJECT PRESENTATIONS I + Context in language processing | ||
| M | 4/30 | 26 | PROJECT PRESENTATIONS II + Wrap-up | ||
| Tu | 5/1 | P4: PROJECTS DUE @ 11:59pm | |||
| Th | 5/10 | 4:00-6:00pm: FINAL EXAM, ICC 106 (regular room) | Study guide | ||
| Visit the GUCL website for NLP talks in Spring 2018 and beyond! | |||||