Update README.md

README.md

@@ -18,7 +18,7 @@ licenses:
 
 ## Introduction
 
-This dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
+This dataset was 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).
 
@@ -32,29 +32,6 @@ The original dataset was distributed in Brat format (https://brat.nlplab.org/sta
 
 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
@@ -111,9 +88,9 @@ enalapril         S0004-06142006000900008-1  234_243  B-NORMALIZABLES
 
 ### Data Splits
 
-* train: 8,074 tokens
-* development: 3,764 tokens
-* test: 3,931 tokens
+* train: 8,130 sentences
+* development: 3,788 sentences
+* test: 3,953 sentences
 
 ## Dataset Creation
 
@@ -129,11 +106,11 @@ For compatibility with similar datasets in other languages, we followed as close
 
 #### Initial Data Collection and Normalization
 
-TO DO
+Manually classified collection of clinical case report sections. The clinical cases were not restricted to a single medical discipline, covering a variety of medical disciplines, including oncology, urology, cardiology, pneumology or infectious diseases. This is key to cover a diverse set of chemicals and drugs.
 
 #### Who are the source language producers?
 
-TO DO
+Humans, there is no machine generated data.
 
 ### Annotations
 
@@ -157,6 +134,29 @@ No personal or sensitive information included.
 
 [email protected]
 
+## 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",
+}
+```
+
 ## 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>.