4306017
doi
10.5281/zenodo.4306017
oai:zenodo.org:4306017
user-medicalnlp
Lima López, Salvador
Barcelona Supercomputing Center
Briva-Iglesias, Vicent
D-REAL
Aguero, Marvin
Barcelona Supercomputing Center
Miranda-Escalada, Antonio
Barcelona Supercomputing Center
Krallinger, Martin
Barcelona Supercomputing Center
ProfNER guidelines - Spanish version
Farré-Maduell, Eulàlia
Barcelona Supercomputing Center
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
NLP
clinical NLP
medical NLP
annotation guidelines
occupations
profner
social media
<p>SMM4H 2021 accepted at NAACL (scheduled in Mexico City in June) <a href="https://www.google.com/url?q=https://2021.naacl.org/&sa=D&source=hangouts&ust=1607428637013000&usg=AFQjCNFw2dB81KiJYjuqunLIaklKkXYJqA">https://2021.naacl.org/</a>.</p>
<p>The ProfNER Shared Task encourages its participants to detect occupations and employment situations in Spanish tweets related to the COVID-19 situation. These guidelines describe the process followed by the clinical and linguist experts who manually annotated the ProfNER corpus. An English translation of the guidelines is <a href="https://doi.org/10.5281/zenodo.4479740">here</a></p>
<p> </p>
<p> </p>
<p><strong>Please, cite:</strong></p>
<p>Miranda-Escalada, A., Farré-Maduell, E., Lima-López, S., Gascó, L., Briva-Iglesias, V., Agüero-Torales, M., & Krallinger, M. (2021, June). The profner shared task on automatic recognition of occupation mentions in social media: systems, evaluation, guidelines, embeddings and corpora. In <em>Proceedings of the Sixth Social Media Mining for Health (# SMM4H) Workshop and Shared Task</em> (pp. 13-20).</p>
<pre><code>@inproceedings{miranda2021profner,
title={The profner shared task on automatic recognition of occupation mentions in social media: systems, evaluation, guidelines, embeddings and corpora},
author={Miranda-Escalada, Antonio and Farr{\'e}-Maduell, Eul{\`a}lia and Lima-L{\'o}pez, Salvador and Gasc{\'o}, Luis and Briva-Iglesias, Vicent and Ag{\"u}ero-Torales, Marvin and Krallinger, Martin},
booktitle={Proceedings of the Sixth Social Media Mining for Health (\# SMM4H) Workshop and Shared Task},
pages={13--20},
year={2021}
}</code></pre>
<p> </p>
<p><strong>Annotation quality:</strong></p>
<p>We have performed a <strong>consistency analysis</strong> of the corpus. 10% of the documents have been annotated by an internal annotator as well as by the linguist experts following these annotation guidelines.</p>
<p>The preliminary Inter-Annotator Agreement (pairwise agreement) is 0.919.</p>
<p> </p>
<p><strong>Please, cite:</strong></p>
<p>Antonio Miranda-Escalada, Eulàlia Farré-Maduell, Salvador Lima-López, Luis Gascó, Vicent Briva-Iglesias, Marvin Agüero-Torales and Martin Krallinger, The ProfNER shared task on automatic recognition of occupation mentions in social media: systems, evaluation, guidelines, embeddings and corpora. In <em>Proceedings of the Sixth Social Media Mining for Health Applications Workshop & Shared Task</em></p>
<p> </p>
<p>For further information, please visit <a href="https://temu.bsc.es/smm4h-spanish/">https://temu.bsc.es/smm4h-spanish/</a> or email us at encargo-pln-life@bsc.es</p>
<p> </p>
<p><strong>Resources:</strong></p>
<ul>
<li><a href="https://temu.bsc.es/smm4h-spanish/"><strong>Web</strong></a></li>
<li><a href="https://doi.org/10.5281/zenodo.4309356"><strong><strong>Gold Standard corpus</strong></strong></a></li>
<li><a href="https://doi.org/10.5281/zenodo.4479740"><strong><strong>Annotation Guidelines (in English)</strong></strong></a></li>
<li><a href="https://doi.org/10.5281/zenodo.4449929"><strong>FastText COVID-19 Twitter embeddings</strong></a></li>
<li><a href="https://doi.org/10.5281/zenodo.4524658"><strong>Occupations gazetteer</strong></a></li>
<li><a href="https://www.aclweb.org/anthology/2021.smm4h-1.3/"><strong>Conference Proceedings</strong></a></li>
</ul>
Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
Zenodo
2020-12-04
info:eu-repo/semantics/report
4306016
user-medicalnlp
1.0
1624868768.427773
518812
md5:459feb423e7835c75edd4d7aec4f2791
https://zenodo.org/records/4306017/files/profner_corpus_guidelines_es.pdf
public
10.5281/zenodo.4306016
isVersionOf
doi