Building and Evaluating Universal Named-Entity Recognition English corpus

TL;DR

This paper introduces a workflow for automatically generating a universal English named-entity recognition corpus using Wikipedia and DBpedia data, with evaluations to improve annotation quality and potential for multilingual extension.

Contribution

The paper presents a novel automated method for creating annotated NER corpora leveraging Wikipedia and DBpedia, applicable across languages.

Findings

Generated an English NER dataset with evaluated annotation quality.

Workflow achieves improvements in precision, recall, and F1-measure.

Dataset and workflow are publicly available for further research.

Abstract

This article presents the application of the Universal Named Entity framework to generate automatically annotated corpora. By using a workflow that extracts Wikipedia data and meta-data and DBpedia information, we generated an English dataset which is described and evaluated. Furthermore, we conducted a set of experiments to improve the annotations in terms of precision, recall, and F1-measure. The final dataset is available and the established workflow can be applied to any language with existing Wikipedia and DBpedia. As part of future research, we intend to continue improving the annotation process and extend it to other languages.