
## Description

**Gold standard annotations for profession detection in Spanish COVID-19 tweets**

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.

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). 

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. 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.

## Files of Named Entity Recognition subtask. 

Content:

- One TSV file per corpus split (train and valid).
- brat: folder with annotations in Brat format. One sub-directory per corpus split (train and valid)
- BIO: folder with corpus in BIO tagging. One file per corpus split (train and valid)
- 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)
- train-valid-txt-files-english: folder with training and validation text files Machine Translated to English.
- 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.
license:cc-by-4.0 region:us
{"license": "cc-by-4.0"}
TAGS
#license-cc-by-4.0 #region-us 


## Description

Gold standard annotations for profession detection in Spanish COVID-19 tweets

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.

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 (URL 

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. 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.

## Files of Named Entity Recognition subtask. 

Content:

- One TSV file per corpus split (train and valid).
- brat: folder with annotations in Brat format. One sub-directory per corpus split (train and valid)
- BIO: folder with corpus in BIO tagging. One file per corpus split (train and valid)
- 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)
- train-valid-txt-files-english: folder with training and validation text files Machine Translated to English.
- 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.
## Description

Gold standard annotations for profession detection in Spanish COVID-19 tweets

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.

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 (URL 

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. 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. ## Files of Named Entity Recognition subtask. 

Content:

- One TSV file per corpus split (train and valid).
- brat: folder with annotations in Brat format. One sub-directory per corpus split (train and valid)
- BIO: folder with corpus in BIO tagging. One file per corpus split (train and valid)
- 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)
- train-valid-txt-files-english: folder with training and validation text files Machine Translated to English.
- 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.
TAGS
#license-cc-by-4.0 #region-us 
 ## Description

Gold standard annotations for profession detection in Spanish COVID-19 tweets

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.

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 (URL 

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. 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. ## Files of Named Entity Recognition subtask. 

Content:

- One TSV file per corpus split (train and valid).
- brat: folder with annotations in Brat format. One sub-directory per corpus split (train and valid)
- BIO: folder with corpus in BIO tagging. One file per corpus split (train and valid)
- 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)
- train-valid-txt-files-english: folder with training and validation text files Machine Translated to English.
- 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.
passage: TAGS
#license-cc-by-4.0 #region-us 
## Description

Gold standard annotations for profession detection in Spanish COVID-19 tweets

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.

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 (URL 

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. 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.## Files of Named Entity Recognition subtask. 

Content:

- One TSV file per corpus split (train and valid).
- brat: folder with annotations in Brat format. One sub-directory per corpus split (train and valid)
- BIO: folder with corpus in BIO tagging. One file per corpus split (train and valid)
- 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)
- train-valid-txt-files-english: folder with training and validation text files Machine Translated to English.
- 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.