After successful completion of the course, students are able to:
- understand and explain the theoretical foundations of modern methods in natural languageprocessing- consider, evaluate, and discuss both ML-based and rule-based solutions to a variety of NLP tasks- consider, evaluate, and discuss the explainability and biases of NLP solutions- understand and address the challenges of NLP for low-resourced languages
Tentative topics:
- Overview of tasks and methods in NLP- Distributional models in NLP- Explainability of NLP models- Understanding and semantics- Semantic parsing and representation
Introductory lectures, individual research work, group discussions
Lectures: 24hCoursework: 35hProject: 16h
Total: 75h
Presentation of individual research outcomes