Course requirements |
This course offers a survey of the state of the art statistical and machine learning methods for computational linguistics. Topic to be covered include:
Prerequisite: Ling 681 or equivalent
The final grade will be based on homework assignments (30%) and a final project (70%).
Through the term, there will be occasional homework assignments to practice the techniques learned in class. These homework assignments will be graded. 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 project to design, implement, document, and evaluate an NLP application based on the machine learning methods cover in the course. The details will depend on the interests of the students.
The required textbook for this course is:
Christopher D. Manning and Hinrich Schütze. 1999. Foundations of Statistical Natural Language Processing. MIT Press.
Additional readings will be made available in class or via the Resources section of the course web page.
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.
Week 1 Introduction
Week 2-4 Non-parametric methods
Week 5-7 Bayesian methods
Week 8-10 Ensemble machines
Week 11-13 Kernel methods
Week 14 Odds and ends
Week 15 Projects
Last modified: Mon Apr 26 09:55:37 PDT 2004