# Building and Evaluating Universal Named-Entity Recognition English corpus

arXiv: https://arxiv.org/abs/2212.07162 · HTML: https://mlsanity.com/paper/2212.07162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection](https://mlsanity.com/paper/1707.02483/) (2017)
- [Introducing RONEC -- the Romanian Named Entity Corpus](https://mlsanity.com/paper/1909.01247/) (2019)
- [Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources](https://mlsanity.com/paper/1908.05758/) (2019)
- [Evaluating Language Tools for Fifteen EU-official Under-resourced Languages](https://mlsanity.com/paper/2010.12428/) (2020)

## Abstract (arXiv)

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.