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
this https URL

通过对具有金融实体和服务提供者关系的网络映射过程的研究，我们介绍了一个针对德国基金募集说明书中公司外包实践的数据集，该数据集专门用于命名实体识别和关系提取任务。我们对 948 个句子进行了标记，并由三名专家进行了标注，共获得了 5,969 个四种实体类型（外包、公司、地点和软件）和 4,102 个关系注释（外包 - 公司、公司 - 地点）。我们训练了最先进的深度学习模型来识别实体和提取关系，初步取得了有希望的结果。该数据集的匿名版本、指南以及用于模型训练的代码均可在此链接中公开获取。