id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
10_1101-2021_02_11_430789	Tyagin, Ilya	Accelerating COVID-19 research with graph mining and transformer-based learning	2021	9	.pdf	application/pdf	9408	807	58	Accelerating COVID-19 research with graph mining and transformer-based learning develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. is currently customized and available in the open domain to massively process COVID-19 related queries. Both systems are the next generation of the AGATHA knowledge network mining transformer model [37]. (1) Most of the existing HG systems are domain-specific (e.g., genedisease interactions) that is usually expressed in limiting the processed information (e.g., significant filtering vocabulary and papers a trained deep bi-LSTM model for extracting predicates from unstructured text. For instance, the node representing the entity "COVID-19" is connected to every sentence and predicate that The prior AGATHA semantic network only includes UMLS terms that appear in SemMedDB predicates [18] which is a major limitation. obtain embeddings per node in the semantic graph, we train AGATHA system ranking model.	./cache/10_1101-2021_02_11_430789.pdf	./txt/10_1101-2021_02_11_430789.txt