Course 048716 - 2010/2011
Advanced Topics in Learning, Systems and Control 2
Machine Learning for Natural Language Processing

Instructor: Koby Crammer
Lectures: :Sunday 12:30-14:30 , Mayer Bldg, Rm. 351

Contact: 3274 or by email (find it here )
Office hours: Tuesday 9:00-10:00 and by appointment

Announcements


Course Plan

Processing written natural language pose many problems: from building words, via parsing sentences, and to understanding what piece of text is about. The course covers topics in machine learning approach for natural language processing, combining statistics and computational linguistics. The course will introduce major tools to represent language, review algorithms for syntax processing and classification, and cover methods for unsupervised learning tools for semantics. We will demonstrate the usefulness of these methods in real world data.

Bibliography

There is no published textbook for the course. Topics will be presented from the following books: More related books are:

Lectures

Lecture  1 (17/10): Introduction
Lecture  2 (24/10): Language Modeling: N-Grams
Lecture  3 (31/10): Regular Expressions and Finite State Automaton
Lecture  4 (07/11): Context Free Grammars
Lecture  5 (14/11): Parsing, Dependency Parsing
Lecture  6 (21/11): Naive Bayes, Maximum Entropy, Log-Linear Models
Lecture  7 (28/11): Random Projections, L1 regularization
                   (5/12): L1 Hanuka break
Lecture  8 (12/12): Feature selection
Lecture  9 (19/12): Complex problems: multi-class, multi-class multi-labeled, structured learning
Lecture 10 (26/12): HMMs, MEMMs, CRFs
Lecture 11 (02/01): Clustering, IBN method
Lecture 12 (09/01): Co-ClusteringLSA
Lecture 13 (16/01): (pLAS, LDA)
Lecture 14 (23/01): Class Presentations

Exercises

Exercise  1 (Due: January 6): New exercise. Data and sample code is . here

Grading