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.
@article{arxiv.2212.07429,
title = {Building Multilingual Corpora for a Complex Named Entity Recognition and Classification Hierarchy using Wikipedia and DBpedia},
author = {Diego Alves and Gaurish Thakkar and Gabriel Amaral and Tin Kuculo and Marko Tadić},
journal= {arXiv preprint arXiv:2212.07429},
year = {2022}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2212.07162