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Ling 681: Statistical Methods in Natural Language Processing (Fall 2006)

by Rob Malouf — last modified 2006-11-14 10:38

This course offers an introduction to statistical methods in computational linguistics. Through a combination of lectures, demonstrations, and hands-on exercises, this course will give students an introduction to the skills necessary for evaluating constructing statistical natural language processing applications and for evaluating their results. Topics to be covered include: basic probability and information theory, statistics for corpus analysis and hypothesis testing, Markov chains and sequence models, probabilistic context-free grammars, stochastic attribute value grammars, and machine learning algorithms.

Available resources

Course type	Lecture
Instructor	Rob Malouf
Time	MWF 13:00–13:50
Location	BA 412

Requirements

The final grade will be based on homework assignments (20%), a take-home midterm exam (30%), and a final project (50%).

Through the term, there will be occasional homework assignments to practice the techniques learned in class. Working in groups is encouraged, but please include the names of all coworkers on the assignment.

The final project for this course will be a group project to design, implement, document, and evaluate an NLP application based on the statistical methods covered in the course.

Readings

The required textbooks for this course are:

Christopher D. Manning and Hinrich Schütze. 1999. Foundations of Statistical Natural Language Processing. MIT Press. http://nlp.stanford.edu/fsnlp/

and

Ian H. Wittien and Eibe Frank. 2005. Data Mining: Practical Machine Learning Tools and Techniques. Second edition. Elsevier. http://www.cs.waikato.ac.nz/∼ml/weka/book.html

They are for sale in the campus bookstore and at Amazon, etc. Updates and corrections to the first book can be downloaded from the authors' websites.

Additional readings will be made available in class or via the "Resources" section of the course web page

Lab

For homework assignments and final projects, we will be using the computational linguistics lab, part of the Social Sciences Research Lab in the basement of the Professional Services and Fine Arts building. Information about how to use the lab will be made available before the first assignment.

Schedule

Week 1–3 Introduction
Background · Mathematical background · Probability · Information Theory
Week 4–6 Statistics
Descriptive statistics · Hypothesis testing · Corpus statistics
Week 7–8 Context-free grammars
Probabilistic context free grammars · Inside-Outside algorithm · Treebank grammars · Dependency-based models
Week 9–10 Attribute-value grammars
Unification · Maximum entropy · Parameter estimation · Parsing
Week 11–12 Machine learning
Word sense disambiguation · Machine learning algorithms · Evaluation
Week 13–14 Text classification
Clustering · Classification · Advanced algorithms · Rainbow · Weka

Prerequisites

Ling 571,581 or permission of instructor.

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