| 1 | Introduction and Overview | |
| 2 | Parsing and Syntax I | |
| 3 | Smoothed Estimation, and Language Modeling | |
| 4 | Parsing and Syntax II | |
| 5 | The EM Algorithm | |
| 6 | The EM Algorithm Part II | |
| 7 | Lexical Similarity | Homework 1 due |
| 8 | Lexical Similarity (cont.) | |
| 9 | Log-Linear Models | |
| 10 | Tagging and History-based Models | |
| 11 | Grammar Induction | Homework 2 due |
| 12 | Computational Modeling of Discourse | |
| 13 | Text Segmentation | |
| Midterm | |
| 14 | Local Coherence and Coreference | Homework 3 due |
| 15 | Machine Translation | |
| 16 | Machine Translation (cont.) | |
| 17 | Machine Translation (cont.) | |
| 18 | Graph-based Methods for NLP Applications | Homework 4 due |
| 19 | Word Sense Disambiguation | |
| 20 | Global Linear Models | |
| 21 | Global Linear Models Part II | Homework 5 due |
| 22 | Dialogue Processing | |
| 23 | Dialogue Processing (cont.) | |
| 24 | Guest Lecture: Stephanie Seneff | Homework 6 due |
| 25 | Text Summarization | |
| Final Exam | |