Building Multilingual Corpora for a Complex Named Entity Recognition and Classification Hierarchy using Wikipedia and DBpedia

Autor:	Alves, Diego, Thakkar, Gaurish, Amaral, Gabriel, Kuculo, Tin, Tadić, Marko
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: substantial text overlap with arXiv:2212.07162
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2212.07429 Zobrazit plný text záznamu View this record from Arxiv