03806ntm a22006017i 4500 000264291 CZ-PrVSE 20220907191439.0 m d cr n|||||||||| 220907s2022 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Khateeb, Ahmad Arsalan ISIS:158046 dis BERT Models in Document Classification eng BERT models in document classification / Ahmad Arsalan Khateeb 2022 ?? stran : digital, PDF soubor Vedoucí práce: Tomáš Kliegr Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2022 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2022 Document classification or categorization with algorithms is a well-known problem in information science that falls under the umbrella term known as natural language processing (NLP). After extracting useful features from the text in a document, machine learning algorithms can be used for this task. This technique can be used for sentiment analysis, spam filtering, and topic labeling. With recent advances in the field of deep learning, neural network-based architectures have achieved state-of-the-art results for many NLP tasks. This thesis deals with the document classification of a COVID-19 Open Research Dataset (CORD-19) subset. The dataset used contains abstracts of scientific articles related to the coronavirus. The goal is to predict how well a paper is cited based on the text within its abstract. Bidirectional Encoder Representations from Transformers or BERT and a related model are used to perform this task. Their performance is compared to much simpler and faster to train conventional machine learning techniques, and the difference in performance is tested for any statistically significant difference. This thesis aims to achieve three main goals: reviewing the literature on document classification using BERT and other machine learning techniques, a theoretical discussion of the methodology used for classification, and a discussion of the results obtained on the mentioned dataset. Způsob přístupu: Internet data analysis and modeling [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas machine learning transformers BERT Covid-19 deep learning neural networks document classification Kliegr, Tomáš ISIS:8484 ths Beranová, Lucie ISIS:118793 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/78427/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/78427 Hlavní práce https://insis.vse.cz/zp/78427/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/78427/posudek/oponent/75751 Oponentura https://insis.vse.cz/zp/78427/priloha/24658 Přiloha k práci https://insis.vse.cz/zp/78427/priloha/24659 Přiloha k práci https://insis.vse.cz/zp/78427/priloha/24660 Přiloha k práci https://insis.vse.cz/zp/78427/priloha/24661 Přiloha k práci https://insis.vse.cz/zp/78427/priloha/24662 Přiloha k práci https://insis.vse.cz/zp/78427/podrobnosti dc:identifier NEPOSILAT VSKP vse78427 220826 78427