---
annotations_creators:
- expert-generated
languages:
- es
multilinguality:
- monolingual
task_categories:
- text-classification
- multi-label-text-classification
task_ids:
- named-entity-recognition
---

# PharmaCoNER Corpus

## BibTeX  citation
If you use these resources in your work, please cite the following paper:

```bibtex
TO DO
```

## Digital Object Identifier (DOI) and access to dataset files

https://zenodo.org/record/4270158#.YTnXP0MzY0F

## Introduction

TO DO: This is a dataset for Named Entity Recognition (NER) from...

### Supported Tasks and Leaderboards

Named Entities Recognition, Language Model

### Languages

ES - Spanish

### Directory structure

* pharmaconer.py
* dev.conll
* test.conll
* train.conll
* README.md

## Dataset Structure

### Data Instances

Three four-column files, one for each split.

### Data Fields

Every file has four columns:
* 1st column: Word form or punctuation symbol 
* 2nd column: Original BRAT file name
* 3rd column: Spans
* 4th column: IOB tag

### Example:
<pre>
La                S0004-06142006000900008-1  123_125  O
paciente          S0004-06142006000900008-1  126_134  O
tenía             S0004-06142006000900008-1  135_140  O
antecedentes      S0004-06142006000900008-1  141_153  O
de                S0004-06142006000900008-1  154_156  O
hipotiroidismo    S0004-06142006000900008-1  157_171  O
,                 S0004-06142006000900008-1  171_172  O
hipertensión      S0004-06142006000900008-1  173_185  O
arterial          S0004-06142006000900008-1  186_194  O
en                S0004-06142006000900008-1  195_197  O
tratamiento       S0004-06142006000900008-1  198_209  O
habitual          S0004-06142006000900008-1  210_218  O
con               S0004-06142006000900008-1  219-222  O
atenolol          S0004-06142006000900008-1  223_231  B-NORMALIZABLES
y                 S0004-06142006000900008-1  232_233  O
enalapril         S0004-06142006000900008-1  234_243  B-NORMALIZABLES
</pre>

### Data Splits

* train: 8,074 tokens
* development: 3,764 tokens
* test: 3,931 tokens

## Dataset Creation

### Methodology

TO DO

### Curation Rationale

For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.

### Source Data

#### Initial Data Collection and Normalization

TO DO

#### Who are the source language producers?

TO DO

### Annotations

#### Annotation process

TO DO

#### Who are the annotators?

TO DO

### Dataset Curators

TO DO: Martin?

### Personal and Sensitive Information

No personal or sensitive information included.

## Contact

TO DO: Casimiro?

## License

<a rel="license" href="https://creativecommons.org/licenses/by/4.0/"><img alt="Attribution 4.0 International License" style="border-width:0" src="https://chriszabriskie.com/img/cc-by.png" width="100"/></a><br />This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Attribution 4.0 International License</a>.