Course Specifications for
CS 388 Natural Language Processing



Course Overview

The intent of the course is to present a fairly broad graduate-level introduction to Natural Language Processing (NLP, a.k.a. comptuational linguistics), the study of computing systems that can process, understand, or communicate in human language. The primary focus of the course will be on understanding various NLP tasks as listed on the course syllabus, algorithms for effectively solving these problems, and methods for evaluating their performance. There will be a focus on statistical learning algorithms that train on (annotated) text corpora to automatically acquire the knowledge needed to perform the task. Class lectures will discuss general issues as well as present abstract algorithms. Implemented versions of some of the algorithms will be provided in order to give a feel for how the systems discussed in class "really work" and allow for extensions and experimentation as part of the course projects.

Course Requirements and Grading

Chapters from the text and a few other readings will be assigned throughout the semester, and the reading should be done before the corresponding class. Copies of the class lecture slides (in Powerpoint) will be available on the course home page. There will be about three homework assignments, a midterm exam, and a final research project.

The homework assignments will involve some programming involving using and building upon existing NLP software packages, and running comptuational experiments to evaluate and analyze these systems. All programming assignments will be in Java. If you do not know Java, you will need to learn it on your own. You can use your student account on the department workstations or any other Java platform available to you (however, we will only provide support for running on departmental Unix machines). If you are not a CS student and need a temporary department account, apply on the web here.

The midterm exam, tentatively scheduled during class on Mon. Oct. 19, will consist of a mix of problem solving and short answer questions covering the material in the first half of the course.

The final project can be a more ambitious experiment or enhancement involving an existing NLP system or a new system implementation. In either case, the implementation and/or experiments should be accompanied by a short paper (about 6 to 7 single-spaced pages) describing the project. An outline for the project report is available here and on the course home page. About a month in advance, you will be asked to submit a one-page project proposal.

Late Submission and Cheating Policies

Homework assignments should be completed independently by each student and any program code should always be appropriately commented. Assignments are due at the beginning of class on the due date. Be sure to hand in assignments on time, late penalties are a loss of a percentage of the original overall points for the assignment: 1 Day: 15%, 2 Days: 40%; 3 Days: 75%; past 3 days: 100%. A day is a 24 hour period starting at the beginning of class and includes all weekend days and holidays.

The preference is for individual final projects from each student; however, larger projects done by pairs of students are possible with prior approval of the instructor.

Read the department's academic policy page. Students who demonstrably violate the Academic Honesty policy will receive a failing grade in the class.

Final Grade

The final grade will be computed as follows: 
36%  Homeworks  
24%  Midterm Exam  
35%  Final Project 
 5%  Class Participation