Legal Entity Recognition

A Dataset of German Legal Documents for Named Entity Recognition


German LER Dataset on :hugs: Hugging Face (Update 2022-10-19)

Now available on Huggingface

Fine-grained Named Entity Recognition in Legal Documents

This work has been partially funded by the project Lynx, which has received funding from the EU's Horizon 2020 research and innovation programme under grant agreement no. 780602, see

Dataset of Legal Documents

Court decisions from 2017 and 2018 were selected for the dataset, published online by the Federal Ministry of Justice and Consumer Protection. 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).

Annotation Guidelines (German)


The dataset consists of 66,723 sentences with 2,157,048 tokens. The sizes of the seven court-specific datasets varies between 5,858 and 12,791 sentences, and 177,835 to 404,041 tokens. The distribution of annotations on a per-token basis corresponds to approx. 19-23 %.

Distribution of Entities

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 %).

Output Format

The dataset is freely available under the CC-BY 4.0 license. The output format is CoNLL-2002. Each line consists of two columns separated by a space. The first column contains a token and the second a tag in IOB2 format. The sentence boundary is marked with an empty line.

Token Tag
Am O
7. O
März O
2006 O
fand O
ein O
Treffen O
der O
saarländischen B-INN
Landesregierung I-INN
unter O
Vorsitz O
des O
Ministerpräsidenten O
Müller B-RR
mit O
Vertretern O
der O
Evangelischen B-ORG
Kirche I-ORG
im I-ORG
Rheinland I-ORG
und O
der O
Evangelischen B-ORG
Kirche I-ORG
der I-ORG
Pfalz I-ORG
statt O
. O



  • CRF-F with features f;
  • CRF-FG with features und gazetteers fg;
  • CRF-FGL with features, gazetteers and lookup table for word similarity fgl.


  • install sklearn-crfsuite and run (modelName=f|fg|fgl): python modelName trainPath testPath

  • Models are saved in models/.



  • BiLSTM-CRF crf;
  • BiLSTM-CRF with char embeddings from BiLSTM blstm-crf;
  • BiLSTM-CNN-CRF with char embeddings from CNN cnn-crf.


  • install BiLSTM-CNN-CRF;
  • copy to folder emnlp2017-bilstm-cnn-crf/ choose a model (modelName=crf|blstm-crf|cnn-crf) and run: python modelName trainPath devPath testPath
  • Models are saved in models/.


old version absl-py==0.7.1 astor==0.8.0 bleach==3.1.0 gast==0.2.2 grpcio==1.23.0 h5py==2.9.0 html5lib==1.0.1 keras==2.2.4 keras-applications==1.0.8 keras-preprocessing==1.1.0 numpy==1.16.4 protobuf==3.9.1 pynif==0.1.4 scipy==1.2.1 six==1.12.0 sklearn-crfsuite==0.3.6 somajo==1.10.5 tensorboard==1.14.0 tensorflow==1.14.0 termcolor==1.1.0 werkzeug==0.15.4


Leitner, E. (2019). Eigennamen- und Zitaterkennung in Rechtstexten. Bachelor’s thesis, Universität Potsdam, Potsdam, 2.

  author       = {Elena Leitner}, 
  title        = {Eigennamen- und Zitaterkennung in Rechtstexten},
  school       = {Universität Potsdam},
  year         = 2019,
  address      = {Potsdam},
  month        = 2,}

Leitner, E., Rehm, G., and Moreno-Schneider, J. (2019). Fine-grained Named Entity Recognition in Legal Documents. In Maribel Acosta, et al., editors, Semantic Systems. The Power of AI and Knowledge Graphs. Proceedings of the 15th International Conference (SEMANTiCS2019), number 11702 in Lecture Notes in Computer Science, pages 272–287, Karlsruhe, Germany, 9. Springer. 10/11 September 2019.

  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
  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 = {}}

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Alle Teilnehmer*innen, Sponsor, Partner, Freiwilligen und Mitarbeiter*innen unseres Hackathons sind verpflichtet, dem Hack Code of Conduct zuzustimmen. Die Organisatoren werden diesen Kodex während der gesamten Veranstaltung durchsetzen. Wir erwarten die Zusammenarbeit aller Teilnehmer*innen, um eine sichere Umgebung für alle zu gewährleisten. Weitere Einzelheiten zum Ablauf der Veranstaltung finden Sie unter Richtlinien in unserem Wiki.

Creative Commons LicenceDie Inhalte dieser Website stehen, sofern nicht anders angegeben, unter einer Creative Commons Attribution 4.0 International License.

Go back