1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CL2024
Hybrid X-Linker: Automated Data Generation and Extreme Multi-label Ranking for Biomedical Entity Linking
Pedro Ruas, Fernando Gallego, Francisco J. Veredas +1
State-of-the-art deep learning entity linking methods rely on extensive human-labelled data, which is costly to acquire. Current datasets are limited in size, leading to inadequate…
cs.CL2024★ 1 cited
ClinLinker: Medical Entity Linking of Clinical Concept Mentions in Spanish
Fernando Gallego, Guillermo López-García, Luis Gasco-Sánchez +2
Advances in natural language processing techniques, such as named entity recognition and normalization to widely used standardized terminologies like UMLS or SNOMED-CT, along with…