---
annotations_creators:
- expert-generated
languages:
- es
multilinguality:
- monolingual
task_categories:
- text-classification
- multi-label-text-classification
task_ids:
- named-entity-recognition
licenses:
- cc-by-4-0
---
# PharmaCoNER Corpus
## Introduction
This dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
It is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).
The annotation of the entire set of entity mentions was carried out by domain experts.
It includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.
The PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets.
The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.
In terms of training examples, this translates to a total of 8130, 3788 and 3953 annotated sentences in each set.
The original dataset was distributed in Brat format (https://brat.nlplab.org/standoff.html).
For further information, please visit [the official website](https://temu.bsc.es/pharmaconer/).
## BibTeX citation
If you use these resources in your work, please cite the following paper:
```bibtex
@inproceedings{,
title = "PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track",
author = "Gonzalez-Agirre, Aitor and
Marimon, Montserrat and
Intxaurrondo, Ander and
Rabal, Obdulia and
Villegas, Marta and
Krallinger, Martin",
booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5701",
doi = "10.18653/v1/D19-5701",
pages = "1--10",
}
```
## Digital Object Identifier (DOI) and access to dataset files
https://zenodo.org/record/4270158#.YTnXP0MzY0F
### Supported Tasks and Leaderboards
Named Entity Recognition
### Languages
ES - Spanish
### Directory structure
* README.md
* pharmaconer.py
* dev-set_1.1.conll
* test-set_1.1.conll
* train-set_1.1.conll
## 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:
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
### 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
The annotation process of the PharmaCoNER corpus was inspired by previous annotation schemes and corpora used for the BioCreative CHEMDNER and GPRO tracks, translating the guidelines used for these tracks into Spanish and adapting them to the characteristics and needs of clinically oriented documents by modifying the annotation criteria and rules to cover medical information needs. This adaptation was carried out in collaboration with practicing physicians and medicinal chemistry experts. The adaptation, translation and refinement of the guidelines (Rabal et al., 2018) was done on a sample set of the SPACCC corpus and linked to an iterative process of annotation consistency analysis through interannotator agreement (IAA) studies until a high annotation quality in terms of IAA was reached. The final, IAA measure obtained for this corpus was calculated on a set of 50 records that were double annotated (blinded) by two different expert annotators, reaching a pairwise agreement of 93% on the exact entity mention comparison level and 76% agreement when also the entity concept normalization was taken into account. Entity normalization was carried out primarily against the SNOMED-CT knowledge base. Note that there is a SNOMED-CT version directly released by the Spanish Ministry of Health twice a year
#### Who are the annotators?
Practicing physicians and medicinal chemistry experts.
### Dataset Curators
The Text Mining Unit from Barcelona Supercomputing center.
### Personal and Sensitive Information
No personal or sensitive information included.
## Contact
encargo-pln-life@bsc.es
## License
This work is licensed under a Attribution 4.0 International License.