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---
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license: cc-by-4.0
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---
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## Description
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**Gold standard annotations for profession detection in Spanish COVID-19 tweets**
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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 addition, it contains the unannotated test, and background sets will be released.
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For Named Entity Recognition, profession detection, annotations are distributed in 2 formats: Brat standoff and TSV. See the Brat webpage for more information about the Brat standoff format (https://brat.nlplab.org/standoff.html).
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The TSV format follows the format employed in SMM4H 2019 Task 2:
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tweet_id | begin | end | type | extraction
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In addition, we provide a tokenized version of the dataset. 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.
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## Files of Named Entity Recognition subtask.
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Content:
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- One TSV file per corpus split (train and valid).
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- brat: folder with annotations in Brat format. One sub-directory per corpus split (train and valid)
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- BIO: folder with corpus in BIO tagging. One file per corpus split (train and valid)
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- train-valid-txt-files: folder with training and validation text files. One text file per tweet. One sub-- directory per corpus split (train and valid)
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- train-valid-txt-files-english: folder with training and validation text files Machine Translated to English.
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- test-background-txt-files: folder with the test and background text files. You must make your predictions for these files and upload them to CodaLab. |
