
Course Description:
The intent of the course is to present a fairly broad graduate-level introduction to Natural Language Processing (NLP, a.k.a. computational 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 and neural-network learning algorithms that train on (annotated) text corpora to automatically acquire the knowledge needed to perform the task.
Course Objectives:
Upon successful completion of the course, the student will be able to:
- Understand the basic principles and challenges of natural language processing.
- Explore the core NLP tasks, such as text preprocessing, language modeling, named entity recognition, and sentiment analysis.
- Gain hands-on experience in implementing and applying NLP techniques using popular libraries and frameworks.
- Analyze and critically evaluate the performance and limitations of NLP systems.
- Understand the ethical considerations and societal implications of NLP technology.
- Teacher: Hussien Seid