Course Description
This course is a graduate level introduction to natural language processing, the primary concern of which is the study of human language from a computational perspective.
The class will cover models at the level of syntactic, semantic and discourse processing. The emphasis will be on corpus-based methods and algorithms, such as Hidden Markov Models and probabilistic context free grammars. We will discuss the use of these methods and models in a variety of applications including syntactic parsing, information extraction, statistical machine translation, and summarization.
This subject qualifies as an Artificial Intelligence and Applications concentration subject.
Readings
Course readings are available in the readings section.
Assessment
Grading Table
| Midterm |
35% |
| Two Homeworks (15% each) |
30% |
| Term Project |
35% |
The project (done alone or in collaboration) on one of the topics covered in the course or some other topic related to language 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 language 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.