# Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources

arXiv: https://arxiv.org/abs/1908.05758 · HTML: https://mlsanity.com/paper/1908.05758/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Building and Evaluating Universal Named-Entity Recognition English corpus](https://mlsanity.com/paper/2212.07162/) (2022)
- [Building Multilingual Corpora for a Complex Named Entity Recognition and Classification Hierarchy using Wikipedia and DBpedia](https://mlsanity.com/paper/2212.07429/) (2022)

## Abstract (arXiv)

With the recent progress in machine learning, boosted by techniques such as deep learning, many tasks can be successfully solved once a large enough dataset is available for training. Nonetheless, human-annotated datasets are often expensive to produce, especially when labels are fine-grained, as is the case of Named Entity Recognition (NER), a task that operates with labels on a word-level. In this paper, we propose a method to automatically generate labeled datasets for NER from public data sources by exploiting links and structured data from DBpedia and Wikipedia. Due to the massive size of these data sources, the resulting dataset -- SESAME Available at https://sesame-pt.github.io -- is composed of millions of labeled sentences. We detail the method to generate the dataset, report relevant statistics, and design a baseline using a neural network, showing that our dataset helps building better NER predictors.