Browsing by Subject "BERT"

Now showing 1 - 7 of 7

Access status: Open Access ,
Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccine
(Wydawnictwa AGH, 2023) Bansal, Anmol; Choudhry, Arjun; Sharma, Anubhav; Susan, Seba
Covid-19 has spread across the world and many different vaccines have been developed to counter its surge. To identify the correct sentiments associated with the vaccines from social media posts, we fine-tune various state-of-the-art pretrained transformer models on tweets associated with Covid-19 vaccines. Specifically, we use the recently introduced state-of-the-art RoBERTa, XLNet, and BERT pre-trained transformer models, and the domain-specific CT-BER and BERTweet transformer models that have been pre-trained on Covid-19 tweets. We further explore the option of text augmentation by oversampling using the language model-based oversampling technique (LMOTE) to improve the accuracies of these models - specifically, for small sample data sets where there is an imbalanced class distribution among the positive, negative and neutral sentiment classes. Our results summarize our findings on the suitability of text oversampling for imbalanced, small-sample data sets that are used to fine-tune state-of-the-art pre-trained transformer models as well as the utility of domain-specific transformer models for the classification task.
Access status: Open Access ,
Enhanced cluster merging and deep learning techniques for entity name identification from biomedical corpus
(Wydawnictwa AGH, 2025) Das, Nilanjana; Dutta, Rakesh; Mondal, Uttam Kumar; Majumder, Mukta; Mandal, Jyotsna Kumar
For mining biomedical information identifying names is the prime task. Complex and uncertain naming styles of biomedical entities are the major setbacks here. Thus, state-of-the-art accuracy of biomedical name identification is reasonably inferior compared to general domain. This study includes Machine Learning and Deep Learning techniques to recognize names from biomedical corpus. In supervised classification, a classifier is built by finding required statistics from training corpus. Accordingly, performance of the system is primarily dependent on quantity and quality of training corpus. But manually preparing a large training dataset with enriched feature samples is laborious and time-taking. Therefore, various techniques were adopted in the literature to make effective use of raw corpora. We have incorporated a novel Cluster Merging technique and Attention Mechanism with BERT embedding for boosting Machine Learning and Deep Learning classifiers respectively. The suggested results outpour that profound techniques are competent and delineate signifying improvement over surviving methods.
Access status: Restricted ,
Opracowanie systemu dialogowego dla aplikacji mobilnej z wykorzystaniem głębokiego uczenia maszynowego
(Data obrony: 2020-07-16) Kotowski, Jakub
Wydział Informatyki, Elektroniki i Telekomunikacji
Access status: Restricted ,
Polish dependency parser
(Data obrony: 2020-07-16) Bartyzel, Karol
Wydział Informatyki, Elektroniki i Telekomunikacji
Access status: Restricted ,
Projekt i implementacja forum internetowego opartego na sugerowanych treściach za pomocą słów pokrewnych wyszukiwań i przeglądanych postów
(Data obrony: 2021-05-17) Ciężadło, Szymon Piotr
Wydział Inżynierii Metali i Informatyki Przemysłowej
Access status: Restricted ,
Rozpoznawanie jednostek nazewniczych w języku polskim
(Data obrony: 2020-07-16) Gajdzica, Piotr
Wydział Informatyki, Elektroniki i Telekomunikacji
Access status: Restricted ,
Sentiment analysis of text with use of attention mechanism
(Data obrony: 2021-01-25) Kawalec, Agata
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej