  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.
