Intelligent Natural Language Processing for Epidemic Intelligence

Abstract

Epidemic Intelligence activities depend significantly on analysts’ ability to locate and aggregate heterogeneous and complex information promptly. The level of novelty of the targeted information is a challenge. The earlier events of interest are located the larger the benefit: more accurate and timely warnings can be made available by the analysts. In this work, the role of Natural Language Processing technologies is investigated. In particular, transformer-based encoding of Web documents (such as newspaper articles as well as epidemic bulletins) for the automatic recognition of events and relevant epidemic information is adopted and evaluated. The resulting framework is configured as a domain-specific meta-search methodology and as a possible basis for a novel generation of Web search environments supporting the Epidemic Intelligence analyst.