Update README.md

README.md

@@ -10,6 +10,8 @@ task_categories:
 - multi-label-text-classification
 task_ids:
 - named-entity-recognition
+licenses:
+- cc-by-4-0
 ---
 
 # PharmaCoNER Corpus
@@ -20,14 +22,15 @@ This dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso
 
 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 medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.
+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 https://temu.bsc.es/pharmaconer/ or send an email to encargo-pln-life@bsc.es
+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:
@@ -49,7 +52,6 @@ If you use these resources in your work, please cite the following paper:
     url = "https://aclanthology.org/D19-5701",
     doi = "10.18653/v1/D19-5701",
     pages = "1--10",
-    abstract = "",
 }
 ```
 
@@ -137,15 +139,15 @@ TO DO
 
 #### Annotation process
 
-TO DO
+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?
 
-TO DO
+ Practicing physicians and medicinal chemistry experts.
 
 ### Dataset Curators
 
-TO DO: Martin?
+The Text Mining Unit from Barcelona Supercomputing center.
 
 ### Personal and Sensitive Information