German BERT Model for Legal Named Entity Recognition

Harshil Darji, Jelena Mitrović, Michael Granitzer

2023

Abstract

The use of BERT, one of the most popular language models, has led to improvements in many Natural Language Processing (NLP) tasks. One such task is Named Entity Recognition (NER) i.e. automatic identification of named entities such as location, person, organization, etc. from a given text. It is also an important base step for many NLP tasks such as information extraction and argumentation mining. Even though there is much research done on NER using BERT and other popular language models, the same is not explored in detail when it comes to Legal NLP or Legal Tech. Legal NLP applies various NLP techniques such as sentence similarity or NER specifically on legal data. There are only a handful of models for NER tasks using BERT language models, however, none of these are aimed at legal documents in German. In this paper, we fine-tune a popular BERT language model trained on German data (German BERT) on a Legal Entity Recognition (LER) dataset. To make sure our model is not overfitting, we performed a stratified 10-fold cross-validation. The results we achieve by fine-tuning German BERT on the LER dataset outperform the BiLSTM-CRF+ model used by the authors of the same LER dataset. Finally, we make the model openly available via HuggingFace.

Paper Citation

in Harvard Style

Darji H., Mitrović J. and Granitzer M. (2023). German BERT Model for Legal Named Entity Recognition. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 723-728. DOI: 10.5220/0011749400003393

in Bibtex Style

@conference{icaart23,
author={Harshil Darji and Jelena Mitrović and Michael Granitzer},
title={German BERT Model for Legal Named Entity Recognition},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={723-728},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011749400003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - German BERT Model for Legal Named Entity Recognition
SN - 978-989-758-623-1
AU - Darji H.
AU - Mitrović J.
AU - Granitzer M.
PY - 2023
SP - 723
EP - 728
DO - 10.5220/0011749400003393