Computational semantics
About
In this course we will discuss ways of representing meaning of words, sentences and conversations with computational methods, both top-down rule-based representations and data-driven representations learned by machine learning. We will contrast them with each other, examine how we can draw inferences or reason with them computationally, and how can they be applied in different language technology tasks and applications.
Topics include:
• natural language meaning, inference, ambiguity and similarity
• formal rule-based representations and techniques
• distributional vector-space models estimated from data
• machine learning-based distributed and contextualised word embeddings and language models
and others.
Entry requirements
Admission to the course requires general entry requirements for second-cycle education and a successful completion of a course in
- programming, 7.5 credits and
- machine learning, 7.5 credits.
or courses giving equivalent skills and knowledge.
English 6 or equivalent is also required.
Application
Do you want to apply for exchange studies at the University of Gothenburg?