Course Meeting Times
Lectures: 2 sessions / week, 1.5 hours / session
Course Description
This course is a graduate level introduction to automatic discourse processing. The emphasis will be on methods and models that have applicability to natural language and speech processing.
The class will cover the following topics: discourse structure, models of coherence and cohesion, plan recognition algorithms, and text segmentation. We will study symbolic as well as machine learning methods for discourse analysis. We will also discuss the use of these methods in a variety of applications ranging from dialogue systems to automatic essay writing.
This subject qualifies as an Artificial Intelligence and Applications concentration subject.
Readings
Course readings are available in the readings section.
Requirements
| ACTIVITIES | PERCENTAGES |
|---|---|
| Class Participation | 10% |
| Term Project | 90% |
The project (done alone or in collaboration) on one of the topics covered in the course or some other topic related to discourse processing will be defined by each class participant in consultation with the professor. These projects will involve:
- a survey of background literature
- implementation of an algorithm for discource processing
- empirical evaluation of the algorithm performance
Academic Integrity
Copying or paraphrasing someone's work (code included), or permitting your own work to be copied or paraphrased, even if only in part, is not allowed, and will result in an automatic grade of 0 for the entire assignment in which the copying or paraphrasing was done.