Dataset and code for Paper "Named Entity Recognition for Analyzing Legal Norms in German Public Administration: A Comparative Study" - dg.o 2026 | Zenodo Skip to main Communities My dashboard Log in Sign up Published April 22, 2026 | Version v1.1 Software Open Dataset and code for Paper "Named Entity Recognition for Analyzing Legal Norms in German Public Administration: A Comparative Study" - dg.o 2026 Authors/Creators Feddoul, Leila 1 Bonagiri, Suresh Kumar 1 Unger, Christoph 1 Bachinger, Sarah T. 1 Erd, Robin 1 Mauch, Marianne 1 König-Ries, Birgitta Show affiliations 1.
Heinz Nixdorf Chair for Distributed Information Systems, Friedrich Schiller University Jena, Jena, Germany Description This repository contains all the code for comparing different approaches to supporting legal norm analysis by detecting specific categories in legal texts to support public service process creation. The directories for the approaches (Deep Discriminative, Deep Generative, and Rulebased) contain the code to replicate the training and prediction. The Evaluation repository contains the code to perform the evaluation of the files that are created during the prediction. To replicate our results, perform the following: 1. Read the README.md for each approach and follow its instructions for training and prediction. 2. Read the README.md in the `.\Evaluation` directory and follow the instructions. The `.\Corpus` directory contains information about the modifications that we applied to our previously created human-annotated corpus [1] and also contains the final splits that we used for training, development, and evaluation. It contains the final splits in two forms: one where all sentences are in a single file, and another where each split is a folder with one file per sentence. This was necessary because of the models' different input structures. [1] Feddoul, Leila, et al. "GerPS-NER: A Dataset for Named Entity Recognition to Support Public Service Process Creation in Germany." SemTech4STLD@ ESWC . 2024. Files model_comparison_dgo2026.zip Files (31.7 MB) Name Size Download all model_comparison_dgo2026.zip md5:b3dc76545c153195829caa21296cf961 31.7 MB Preview Download 229 Views 92 Downloads Show more details All versions This version Views Total views 229 38 Downloads Total downloads 92 16 Data volume Total data volume 2.9 GB 507.7 MB More info on how stats are collected.... Versions External resources Indexed in OpenAIRE Communities Keywords and subjects Keywords Machine Learning Named Entity Recognition Public Administration Law e-Government Details DOI DOI Badge DOI 10.5281/zenodo.19696096 Markdown [](https://doi.org/10.5281/zenodo.19696096) reStructuredText .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.19696096.svg :target: https://doi.org/10.5281/zenodo.19696096 HTML <a href="https://doi.org/10.5281/zenodo.19696096"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.19696096.svg" alt="DOI"></a> Image URL https://zenodo.org/badge/DOI/10.5281/zenodo.19696096.svg Target URL https://doi.org/10.5281/zenodo.19696096 Resource type Software Publisher Zenodo Conference 27th Annual International Conference on Digital Government Research (Dg.o2026) , University of Nebraska at Omaha, Omaha, Nebraska, USA, 01-04 June Rights License Creative Commons Attribution 4.0 International The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. Read more Citation Export Technical metadata Created April 22, 2026 Modified April 22, 2026 Jump up About About Policies Infrastructure Principles Projects Roadmap Contact Blog Blog Support Help FAQ Developers REST API OAI-PMH Contribute GitHub Donate Funded by Powered by CERN Data Centre & InvenioRDM Status Privacy policy Cookie policy Terms of Use This site uses cookies. Find out more on how we use cookies Accept all cookies Accept only essential cookies