NED SERIES
How to extract knowledge from biomedical texts by combining pre-trained language models with graph machine learning
This article summarizes an article accepted by the IEEE. Application of Information and Communications Technologies (AICT2024) conference. In addition to the undersigned, Felice Paolo Colliani (first author), Giovanni Garifo, Antonio Vetroand Juan Carlos De Martin are the co-authors of this article.
The biomedical domain has seen a steadily increasing publication rate over the years due to the growth in scientific research, advancements in technology, and global emphasis on healthcare and medical research.
The application of natural language processing (NLP) techniques in the biomedical field represents a change in the analysis and interpretation of the vast corpus of biomedical knowledge, improving our ability to derive meaningful insights from textual data.
Named entity disambiguation (NED) is a critical NLP task that involves resolving ambiguities in entity mentions by linking them to the correct entries in a knowledge base. To understand the importance and complexity of such a task, consider the following example:
Zika It belongs to the Flaviviridae family and is transmitted by the Aedes mosquito.
Individuals affected by Zika infection often…
