Building and Evaluating Universal Named-Entity Recognition English corpus
This work addresses the need for scalable, language-agnostic datasets for NLP researchers, though it is incremental as it builds on existing frameworks and data sources.
The authors tackled the problem of generating automatically annotated corpora for Named-Entity Recognition by applying a Universal Named Entity framework to Wikipedia and DBpedia data, resulting in an English dataset with improvements in precision, recall, and F1-measure.
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.