NED-SERIES
Easy methods to distill information from biomedical textual content combining pre-trained language fashions with graph machine studying
This text synthesizes a paper accepted for the IEEE Utility of Info and Communication Applied sciences (AICT2024) convention. Along with the undersigned, Felice Paolo Colliani (first creator), Giovanni Garifo, Antonio Vetrò, and Juan Carlos De Martin are the co-authors of this paper.
The biomedical area has seen a steadily rising publication price over time as a result of development of scientific analysis, advances in know-how, and the worldwide emphasis on healthcare and medical analysis.
The appliance of Pure Language Processing (NLP) methods within the biomedical area represents a shift within the evaluation and interpretation of the huge corpus of biomedical information, enhancing our capacity to derive significant insights from textual information.
Named Entity Disambiguation (NED) is a important NLP activity that includes resolving ambiguities in entity mentions by linking them to the proper entries in a information base. To know the significance and complexity of such a activity, think about the next instance:
Zika belongs to the Flaviviridae household and it’s unfold by Aedes mosquitoes.
People affected by Zika an infection typically…
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