Automatic Annotation of Disposition Counts in News Articles
News media aggregate and report disposition counts during crises: how many people are affected, suspected affected, have died, and have recovered or been recovered; and they tend to do so in a timely and trustworthy manner. We present and evaluate a method for identifying these counts in unstructured natural language text, supporting downstream tasks such as automatic creation of epidemic curves.
Authors
- Dave Carter (ORCID: https://orcid.org/0000-0003-3503-7615)
- Simon Rodier (ORCID: https://orcid.org/0000-0002-6797-629X)
Institutions
- National Research Council Canada (CA)
Publication Details
- Journal
- Studies in health technology and informatics
- Published
- 2025-05-15
- DOI
- https://doi.org/10.3233/shti250502
- Primary Topic
- Data-Driven Disease Surveillance
- Type
- article
- Field-Weighted Citation Impact
- 0.00