This course presents an introduction to Natural language processing (NLP) with an emphasis on computational semantics i.e. the process of constructing and reasoning with meaning representations of natural language text.
Learning objective
The objective of the course is to learn about various topics in computational semantics and its importance in natural language processing methodology and research. Exercises and the project will be key parts of the course so the students will be able to gain hands-on experience with state-of-the-art techniques in the field.
Content
We will take a modern view of the topic, and focus on various statistical and deep learning approaches for computation semantics. We will also overview various primary areas of research in language processing and discuss how the computational semantics view can help us make advances in NLP.
Lecture notes
Lecture slides will be made available at the course Web site.
Literature
No textbook is required, but there will be regularly assigned readings from research literature, linked to the course website.
Prerequisites / Notice
The student should have successfully completed a graduate level class in machine learning (252-0220-00L), deep learning (263-3210-00L) or natural language processing (252-3005-00L) before. Similar courses from other universities are acceptable too.
Performance assessment
Performance assessment information (valid until the course unit is held again)
Repetition only possible after re-enrolling for the course unit.
Additional information on mode of examination
The final assessment will be a combination of classroom participation, graded exercises and the project. There will be 2 exercise sets which will be a mix of theoretical and implementation problems, and will total to 30% of your grade. Classroom participation (including a research paper presentation) will account for 20% of the grade. The project will account of the rest of the grade (50%). There will be no written exams.
Performance assessment as a semester course