Simulation and annotation of global acronyms.
Bioinformatics
; 38(11): 3136-3138, 2022 May 26.
Article
em En
| MEDLINE
| ID: mdl-35482480
ABSTRACT
MOTIVATION Global acronyms are used in written text without their formal definitions. This makes it difficult to automatically interpret their sense as acronyms tend to be ambiguous. Supervised machine learning approaches to sense disambiguation require large training datasets. In clinical applications, large datasets are difficult to obtain due to patient privacy. Manual data annotation creates an additional bottleneck. RESULTS:
We proposed an approach to automatically modifying scientific abstracts to (i) simulate global acronym usage and (ii) annotate their senses without the need for external sources or manual intervention. We implemented it as a web-based application, which can create large datasets that in turn can be used to train supervised approaches to word sense disambiguation of biomedical acronyms. AVAILABILITY AND IMPLEMENTATION The datasets will be generated on demand based on a user query and will be downloadable from https//datainnovation.cardiff.ac.uk/acronyms/.
Texto completo:
1
Base de dados:
MEDLINE
Tipo de estudo:
Guideline
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Reino Unido