Thesis Details
Klasifikace vztahů mezi pojmenovanými entitami v textu
This master thesis deals with the extraction of relationships between named entities in the text. In the theoretical part of the thesis, the issue of natural language representation for machine processing is discussed. Subsequently, two partial tasks of relationship extraction are defined, namely named entities recognition and classification of relationships between them, including a summary of state-of-the-art solutions. In the practical part of the thesis, system for automatic extraction of relationships between named entities from downloaded pages is designed. The classification of relationships between entities is based on the pre-trained transformers. In this thesis, four pre-trained transformers are compared, namely BERT, XLNet, RoBERTa and ALBERT.
relationship extraction, named-entity recognition, transformers, BERT, ALBERT, RoBERTa, XLNet, fine-tuning
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT), člen
Hrubý Martin, Ing., Ph.D. (DITS FIT BUT), člen
Kočí Radek, Ing., Ph.D. (DITS FIT BUT), člen
Rozman Jaroslav, Ing., Ph.D. (DITS FIT BUT), člen
@mastersthesis{FITMT22682,
author = "Karel Ond\v{r}ej",
type = "Master's thesis",
title = "Klasifikace vztah\r{u} mezi pojmenovan\'{y}mi entitami v textu",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2020,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/22682/"
}