Update README.md

README.md

@@ -17,7 +17,8 @@ language:
 
 ### Dataset Summary
 
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+This dataset is initially created for the BSNLP Shared Task 2019 and reported in the conference paper "The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages"
+It is further improved in "Reconstructing NER Corpora: a Case Study on Bulgarian" and finally transformed in a csv format appropriate for token classification in Huggingface.
 
 ### Supported Tasks and Leaderboards
 
@@ -39,7 +40,7 @@ This dataset card aims to be a base template for new datasets. It has been gener
 
 ### Data Splits
 
-[More Information Needed]
+train, test
 
 ## Dataset Creation
 
@@ -97,7 +98,38 @@ This dataset card aims to be a base template for new datasets. It has been gener
 
 ### Citation Information
 
-[More Information Needed]
+@inproceedings{piskorski-etal-2019-second,
+   title = "The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across {S}lavic Languages",
+   author = "Piskorski, Jakub  and Laskova, Laska  and Marci{\'n}czuk, Micha{\l}  and Pivovarova, Lidia  and P{\v{r}}ib{\'a}{\v{n}}, Pavel  
+   and Steinberger, Josef  and Yangarber, Roman",
+   booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
+   month = aug,
+   year = "2019",
+   address = "Florence, Italy",
+   publisher = "Association for Computational Linguistics",
+   url = "https://www.aclweb.org/anthology/W19-3709",
+   pages = "63--74"
+   }
+
+   @inproceedings{marinova-etal-2020-reconstructing,
+    title = "Reconstructing {NER} Corpora: a Case Study on {B}ulgarian",
+    author = "Marinova, Iva  and
+      Laskova, Laska  and
+      Osenova, Petya  and
+      Simov, Kiril  and
+      Popov, Alexander",
+    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2020.lrec-1.571",
+    pages = "4647--4652",
+    abstract = "The paper reports on the usage of deep learning methods for improving a Named Entity Recognition (NER) training corpus and for predicting and annotating new types in a test corpus. We show how the annotations in a type-based corpus of named entities (NE) were populated as occurrences within it, thus ensuring density of the training information. A deep learning model was adopted for discovering inconsistencies in the initial annotation and for learning new NE types. The evaluation results get improved after data curation, randomization and deduplication.",
+    language = "English",
+    ISBN = "979-10-95546-34-4",
+}
+
 
 ### Contributions