---
annotations_creators:
- expert-generated
languages:
- es
multilinguality:
- monolingual
task_categories:
- text-classification
- multi-label-text-classification
task_ids:
- named-entity-recognition
---
# CANTEMIST Corpus
## BibTeX citation
If you use these resources in your work, please cite the following paper:
```bibtex
@inproceedings{miranda2020named,
title={Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results},
author={Miranda-Escalada, A and Farr{\'e}, E and Krallinger, M},
booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings},
year={2020}
}
```
## Digital Object Identifier (DOI) and access to dataset files
TO DO: link to zenodo
## 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
* cantemist-ner.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:
El cc_onco101 662_664 O
informe cc_onco101 665_672 O
HP cc_onco101 673_675 O
es cc_onco101 676_678 O
compatible cc_onco101 679_689 O
con cc_onco101 690_693 O
adenocarcinoma cc_onco101 694_708 B-MORFOLOGIA_NEOPLASIA
moderadamente cc_onco101 709_722 I-MORFOLOGIA_NEOPLASIA
diferenciado cc_onco101 723_735 I-MORFOLOGIA_NEOPLASIA
que cc_onco101 736_739 O
afecta cc_onco101 740_746 O
a cc_onco101 747_748 O
grasa cc_onco101 749_754 O
peripancreática cc_onco101 755_770 O
sobrepasando cc_onco101 771_783 O
la cc_onco101 784_786 O
serosa cc_onco101 787_793 O
, cc_onco101 793_794 O
infiltración cc_onco101 795_807 O
perineural cc_onco101 808_818 O
. cc_onco101 818_819 O
### Data Splits
* train: 18,916 tokens
* development: 17,656 tokens
* test: 10,886 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
This work is licensed under a Attribution 4.0 International License.