  With the ever-growing popularity of the field of NLP, the demand for datasets
in low resourced-languages follows suit. Following a previously established
framework, in this paper, we present the UNER dataset, a multilingual and
hierarchical parallel corpus annotated for named-entities. We describe in
detail the developed procedure necessary to create this type of dataset in any
language available on Wikipedia with DBpedia information. The three-step
procedure extracts entities from Wikipedia articles, links them to DBpedia, and
maps the DBpedia sets of classes to the UNER labels. This is followed by a
post-processing procedure that significantly increases the number of identified
entities in the final results. The paper concludes with a statistical and
qualitative analysis of the resulting dataset.
