Update README.md

README.md

@@ -18,7 +18,7 @@ task_categories:
 task_ids:
 - named-entity-recognition
 paperswithcode_id: legal-documents-entity-recognition
-pretty_name: Legal Documents Entity Recognition
+pretty_name: Named Entity Recognition in Legal Documents
 ---
 
 # Dataset Card for Legal Documents Entity Recognition
@@ -53,19 +53,19 @@ pretty_name: Legal Documents Entity Recognition
 - **Repository:** None
 - **Paper:** https://link.springer.com/chapter/10.1007/978-3-030-33220-4_20
 - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
-- **Point of Contact:** Georg Rehm (georg.rehm@dfki.de)
+- **Point of Contact:** Elena Leitner (elena.leitner@dfki.de), Georg Rehm (georg.rehm@dfki.de)
 
 ### Dataset Summary
 
-[More Information Needed]
+The dataset consists of 66,723 sentences with 2,157,048 tokens. The dataset includes two different versions of annotations, one with a set of 19 fine-grained semantic classes and another one with a set of 7 coarse-grained classes. There are 53,632 annotated entities in total, the majority of which (74.34 %) are legal entities, the others are person, location and organization (25.66 %).
 
 ### Supported Tasks and Leaderboards
 
-[More Information Needed]
+Named Entity Recognition in legal Documents
 
 ### Languages
 
-[More Information Needed]
+German
 
 ## Dataset Structure
 
@@ -79,7 +79,8 @@ pretty_name: Legal Documents Entity Recognition
 
 ### Data Splits
 
-[More Information Needed]
+-
+
 ## Dataset Creation
 
 ### Curation Rationale
@@ -88,7 +89,7 @@ pretty_name: Legal Documents Entity Recognition
 
 ### Source Data
 
-[More Information Needed]
+Court decisions from 2017 and 2018 were selected for the dataset, published online by the [Federal Ministry of Justice and Consumer Protection](http://www.rechtsprechung-im-internet.de). The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG). 
 
 #### Initial Data Collection and Normalization
 
@@ -100,7 +101,28 @@ pretty_name: Legal Documents Entity Recognition
 
 ### Annotations
 
-[More Information Needed]
+The source texts were manually annotated with 19 semantic classes: 
+- person
+- judge
+- lawyer
+- country
+- city
+- street
+- landscape
+- organization
+- company
+- institution
+- court
+- brand
+- law
+- ordinance
+- European legal norm
+- regulation
+- contract
+- court decision
+- and legal literature.
+
+The 19 fine-grained classes were automatically generalised to seven more coarse-grained classes (person, location, organization, legal norm, case-by-case regulation, court decision, and legal literature).
 
 #### Annotation process
 
@@ -136,11 +158,30 @@ pretty_name: Legal Documents Entity Recognition
 
 ### Licensing Information
 
-[More Information Needed]
+<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
 
 ### Citation Information
-
-[More Information Needed]
+```
+@inproceedings{leitner2019fine,
+  author = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider},
+  title = {{Fine-grained Named Entity Recognition in Legal Documents}},
+  booktitle = {Semantic Systems. The Power of AI and Knowledge
+                  Graphs. Proceedings of the 15th International Conference
+                  (SEMANTiCS 2019)},
+  year = 2019,
+  editor = {Maribel Acosta and Philippe Cudré-Mauroux and Maria
+                  Maleshkova and Tassilo Pellegrini and Harald Sack and York
+                  Sure-Vetter},
+  keywords = {aip},
+  publisher = {Springer},
+  series = {Lecture Notes in Computer Science},
+  number = {11702},
+  address = {Karlsruhe, Germany},
+  month = 9,
+  note = {10/11 September 2019},
+  pages = {272--287},
+  pdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}}
+```
 ### Contributions
 
 Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.