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Lecture Notes

lec # TOPICS
1 Introduction and Overview (PDF)
2 Basic Language Statistics; Zipf's Law (PDF)
3 Language Models; Smoothed Estimation (PDF)
4 Tagging; Transformation Based Learning; HMM Taggers (PDF)
5 Maximum Entropy Tagger (PDF)
6 Introduction to Syntax; Probabilistic Context Free Grammars
7 Syntactic Parsing
8 Introduction to EM
9 Unsupervised Grammar Induction (PDF)
10 Distributional Similarity; Clustering (PDF)
11 Distributional Similarity (cont.) (PDF)
12 Word Sense Disambiguation; Co-training (PDF)
13 Text Segmentation (PDF)
14 Learning Discourse Structure (PDF)
15 Rhetorical Parsing (PDF)
16 Text Summarization (PDF)
17 Text Summarization (cont.)
18 Midterm
19 Anaphora Resolution (PDF)
20 Machine Translation I (PDF) (Courtesy of Philipp Koehn. Used with permission.)
21 Machine Translation II (PDF) (Courtesy of Philipp Koehn. Used with permission.)
22 Machine Translation II (cont.)
23 Project Presentations
24 Project Presentations (cont.)