# CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction

arXiv: https://arxiv.org/abs/2403.15322 · HTML: https://mlsanity.com/paper/2403.15322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports](https://mlsanity.com/paper/2208.02140/) (2022)
- [KazNERD: Kazakh Named Entity Recognition Dataset](https://mlsanity.com/paper/2111.13419/) (2021)
- [A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events](https://mlsanity.com/paper/2004.03283/) (2020)
- [A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products](https://mlsanity.com/paper/2004.03287/) (2020)
- [KIND: an Italian Multi-Domain Dataset for Named Entity Recognition](https://mlsanity.com/paper/2112.15099/) (2021)
- [Building a Corpus for Biomedical Relation Extraction of Species Mentions](https://mlsanity.com/paper/2306.08403/) (2023)

## Abstract (arXiv)

The process of cyber mapping gives insights in relationships among financial entities and service providers. Centered around the outsourcing practices of companies within fund prospectuses in Germany, we introduce a dataset specifically designed for named entity recognition and relation extraction tasks. The labeling process on 948 sentences was carried out by three experts which yields to 5,969 annotations for four entity types (Outsourcing, Company, Location and Software) and 4,102 relation annotations (Outsourcing-Company, Company-Location). State-of-the-art deep learning models were trained to recognize entities and extract relations showing first promising results. An anonymized version of the dataset, along with guidelines and the code used for model training, are publicly available at https://www.dfki.uni-kl.de/cybermapping/data/CO-Fun-1.0-anonymized.zip.