  We introduce the Universal Named-Entity Recognition (UNER)framework, a
4-level classification hierarchy, and the methodology that isbeing adopted to
create the first multilingual UNER corpus: the SETimesparallel corpus annotated
for named-entities. First, the English SETimescorpus will be annotated using
existing tools and knowledge bases. Afterevaluating the resulting annotations
through crowdsourcing campaigns,they will be propagated automatically to other
languages within the SE-Times corpora. Finally, as an extrinsic evaluation, the
UNER multilin-gual dataset will be used to train and test available NER tools.
As part offuture research directions, we aim to increase the number of
languages inthe UNER corpus and to investigate possible ways of integrating
UNERwith available knowledge graphs to improve named-entity recognition.
