Breadcrumb

Computational semantics

Course
LT2813
Master’s level
7.5 credits (ECTS)
Study pace
50%
Time
Day
Location
Location independent
Study form
Distance
Language
English
Duration
-
Application period
-
Application code
GU-24024
Tuition
Full education cost: 18 000 SEK
First payment: 18 000 SEK

No fees are charged for EU and EEA citizens, Swedish residence permit holders and exchange students.

More information about tuition fees

Summary

The course introduces students to the current AI methods of modelling natural language and meaning it expresses.

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.

Prerequisites and selection

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.

Assessment of qualifications

Assessment is based upon the number of credits from previous university studies and the fit of the candidates academic background to the course pre-requisities.

To access their eligibility for the course, candidates are requested to fill in a summary sheet  and upload it to the admissions system.
Summary sheet for application

Selection

Selection is based upon the number of credits from previous university studies, maximum 165 credits.