Update README.md

README.md

@@ -6,101 +6,75 @@ languages:
 multilinguality:
 - monolingual
 task_categories:
+- token-classification
 - text-classification
 - multi-label-text-classification
 task_ids:
 - named-entity-recognition
+licenses:
+- cc-by-4-0
 ---
-
 # 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}
-}
-```
-
+## Dataset Description
+### Dataset Summary
+Collection of 1301 oncological clinical case reports written in Spanish, , with tumor morphology mentions annotated and mapped to a controlled terminology, eCIE-O. The training subset contains 501 documents, the development subsets 500, and the test subset 300. 
+All documents of the corpus have been manually annotated by clinical experts with mentions of tumor morphology (in Spanish, “morfología de neoplasia”). Every tumor morphology mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O).
+This dataset was designed for the CANcer TExt Mining Shared Task, sponsored by [Plan de Impulso de las Tecnologías del Lenguaje (Plan-TL)](https://plantl.mineco.gob.es/Paginas/index.aspx).
+For further information, please visit [the official website](https://temu.bsc.es/cantemist/).
 ## 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
-
+https://zenodo.org/record/3978041
+### Supported Tasks
+Named Entity Recognition
 ### Languages
-
 ES - Spanish
-
-### Directory structure
-
-* cantemist-ner.py
-* dev.conll
-* test.conll
-* train.conll
+### Directory Structure
 * README.md
-
+* cantemist.py
+* TODO
+* TODO
+* TODO
 ## 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 
+Every file has 4 columns:
+* 1st column: Word form or punctuation symbol
 * 2nd column: Original BRAT file name
 * 3rd column: Spans
 * 4th column: IOB tag
-
-### Example:
+#### Example
 <pre>
-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
+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
 </pre>
 
 ### Data Splits
 
-* train: 18,916 tokens
-* development: 17,656 tokens
-* test: 10,886 tokens
+* train: 18,916 tokens (TODO)
+* development: 17,656 tokens (TODO)
+* test: 10,886 tokens (TODO)
 
 ## Dataset Creation
 
-### Methodology
-
-TO DO
-
 ### Curation Rationale
 
 For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
@@ -109,34 +83,54 @@ For compatibility with similar datasets in other languages, we followed as close
 
 #### Initial Data Collection and Normalization
 
-TO DO
+TODO
 
 #### Who are the source language producers?
 
-TO DO
+Humans, there is no machine generated data.
 
 ### Annotations
 
 #### Annotation process
 
-TO DO
+The dataset was annotated performing regular quality control analysis and following strict guidelines.
 
 #### Who are the annotators?
 
-TO DO
+Clinical experts.
+
+### Personal and Sensitive Information
+
+No personal or sensitive information included.
+
+## Additional Information
 
 ### Dataset Curators
 
-TO DO: Martin?
+The Text Mining Unit from Barcelona Supercomputing center.
 
-### Personal and Sensitive Information
+### Contact
 
-No personal or sensitive information included.
+encargo-pln-life@bsc.es
+
+### Citation Information
+
+If you use these resources in your work, please cite the following paper:
+
+```bibtex
+@article{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, Antonio and Farr{\'e}, Eul{\`a}lia and Krallinger, Martin},
+  journal={IberLEF@ SEPLN},
+  pages={303--323},
+  year={2020}
+}
+```
 
-## Contact
+### Funding
 
-TO DO: Casimiro?
+This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
 
-## License
+### Licensing Information
 
-<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>.
+<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>.