Computational Models of Concept Similarity for the Estonian Language
Name
Claudia Kittask
Abstract
The purpose of this thesis is to test and compare different computational models of similarity for the Estonian language. Models' predictions for words and concepts similarity is usually compared against human predictions. To make such comparisons between models' similarity estimates and human scores, a proper human annotated data set had to be created for the Estonian language. The SimLex-999 data set was chosen for translation into Estonian. This resource is used to test three families of computational models of similarity: distributional models, semantic networks and computer vision models. The results of this thesis can be used to evaluate future similarity models.
Graduation Thesis language
English
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Eduard Barbu
Defence year
2019