A SURVEY OF TRANSFORMER AND LARGE LANGUAGE MODEL STRATEGIES FOR CLINICAL ABBREVIATION DISAMBIGUATION: A SPECTRUM OF PARADIGMS

The clinical narrative is a treasure trove of patient information, yet its value is often obscured by a high density of ambiguous abbreviations. For years, resolving this ambiguity was a classic supervised learning problem, held back by data scarcity and the need to create custom models for each cas...

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Principais autores:	Ghada Farouk, Sally Saad, Mostafa Aref
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Ain Shams University, Faculty of Computer and Information Science 2026-03-01
coleção:	International Journal of Intelligent Computing and Information Sciences
Assuntos:	Natural Language Processing (NLP) Word Sense Disambiguation (WSD) Transformers Generative Models In-context Learning
Acesso em linha:	https://ijicis.journals.ekb.eg/article_488279_70ece93f1ad06cded4375260571f0232.pdf
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