Knowledge-enhanced information extraction across languages for pharmacovigilance
The present project aims to design Artificial Intelligence (AI) methods that automatically digest these different types of text sources and jointly extract such knowledge and observations in order to populate existing knowledge bases. Our project showcases these methods in the domain of pharmacovigilance, which endeavors to maintain up-to-date knowledge on adverse drug reactions (ADRs) for the benefit of public health. In this domain, authoritative sources include scientific journals and drug labels while elementary observations are reported in patient records and social media.
Pierre Zweigenbaum; CNRS-LIMSI -
Sebastian Möller; DFKI, Speech and Language Technology Lab -
Yuji Matsumoto; NAIST Graduate School of Science and Technology Nara Institute of Science and Technology