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- Title
Simulation and annotation of global acronyms.
- Authors
Filimonov, Maxim; Chopard, Daphné; Spasić, Irena
- 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/.
- Subjects
SUPERVISED learning; ACRONYMS; WEB-based user interfaces; MACHINE learning
- Publication
Bioinformatics, 2022, Vol 38, Issue 11, p3136
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btac298