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Dataset Open Access

ProfNER corpus: gold standard annotations for profession detection in Spanish COVID-19 tweets

Miranda-Escalada, Antonio; Briva-Iglesias, Vicent; Farré, Eulàlia; Lima López, Salvador; Aguero, Marvin; Krallinger, Martin


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  <dc:creator>Miranda-Escalada, Antonio</dc:creator>
  <dc:creator>Briva-Iglesias, Vicent</dc:creator>
  <dc:creator>Farré, Eulàlia</dc:creator>
  <dc:creator>Lima López, Salvador</dc:creator>
  <dc:creator>Aguero, Marvin</dc:creator>
  <dc:creator>Krallinger, Martin</dc:creator>
  <dc:date>2020-12-07</dc:date>
  <dc:description>
THERE IS A NEWER VERSION (1.3) THAT INCORPORATES THE UNANNOTATED TEST AND BACKGROUND FILES 


 

Gold Standard annotations for SMM4H-Spanish shared task. SMM4H 2021 accepted at NAACL (scheduled in Mexico City in June) https://2021.naacl.org/.


Introduction:
The entire corpus contains 10,000 annotated tweets. It has been split into training, validation and test (60-20-20). The current version contains the training and development set of the shared task with Gold Standard annotations.
In future versions of the dataset, test and background sets will be released.

For the subtask-1 (classification), annotations are distributed in a tab-separated file (TSV). The TSV format follows the format employed in SMM4H 2019 Task 2:
tweet_id    class
 
For the subtask-2 (Named Entity Recognition, profession detection), annotations are distributed in 2 formats: Brat standoff and TSV. See Brat webpage for more information about Brat standoff format (https://brat.nlplab.org/standoff.html). The TSV format follows the format employed in SMM4H 2019 Task 2:
tweet_id    begin    end    type    extraction

In addition, we provide a tokenized version of the dataset, for participant's convenience. It follows the BIO format (similar to CONLL). The files were generated with the brat_to_conll.py script (included), which employs the es_core_news_sm-2.3.1 Spacy model for tokenization.


Zip structure:

txt-files: folder with text files. One text file per tweet. One sub-directory per corpus split (train and valid).

txt-files-english: folder with text files Machine Translated to English.

subtask-1: One file per corpus split (train.tsv and valid.tsv).

subtask-2:


	brat: folder with annotations in Brat format. One sub-directory per corpus split (train and valid).
	tsv: folder with annotations in TSV. One file per corpus split (train and valid).
	BIO: folder with corpus in BIO tagging. One file per corpus split (train and valid).


 

Annotation quality:

We have performed a consistency analysis of the corpus. 10% of the documents have been annotated by an internal annotator as well as by the linguist experts following the same annotation guideliens.

The preliminary Inter-Annotator Agreement (pairwise agreement) is 0.919.

 


Important shared task information:
SYSTEM PREDICTIONS MUST FOLLOW THE TSV FORMAT. And systems will only be evaluated for the PROFESION and SITUACION_LABORAL predictions (despite the Gold Standard contains 2 extra entity classes). For more information about the evaluation scenario, see the Codalab link, or the evaluation webpage.

 

For further information, please visit https://temu.bsc.es/smm4h-spanish/ or email us at encargo-pln-life@bsc.es
 

Do not share the data with other individuals/teams without permission from the task organizer. Tweets IDs are the primary source of information. Tweet texts are provided as support material. By downloading this resource, you agree to the Twitter Terms of Service, Privacy Policy, Developer Agreement, and Developer Policy.

 

Resources:


	Web
	Annotation guidelines (in Spanish)
	Annotation guidelines (in English)
	FastText COVID-19 Twitter embeddings
	Occupations gazetteer
</dc:description>
  <dc:description>Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).</dc:description>
  <dc:identifier>https://zenodo.org/record/4522129</dc:identifier>
  <dc:identifier>10.5281/zenodo.4522129</dc:identifier>
  <dc:identifier>oai:zenodo.org:4522129</dc:identifier>
  <dc:language>spa</dc:language>
  <dc:relation>doi:10.5281/zenodo.4309356</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/medicalnlp</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>NLP</dc:subject>
  <dc:subject>clinical NLP</dc:subject>
  <dc:subject>Gold Standard</dc:subject>
  <dc:subject>Occupations</dc:subject>
  <dc:subject>Social Media</dc:subject>
  <dc:subject>Twitter</dc:subject>
  <dc:subject>NER</dc:subject>
  <dc:subject>Professions</dc:subject>
  <dc:subject>smm4h</dc:subject>
  <dc:subject>profner</dc:subject>
  <dc:title>ProfNER corpus: gold standard annotations for profession detection in Spanish COVID-19 tweets</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>
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