  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.
