Abstract
Relation extraction is essential for extracting and understanding biographical information in the context of digital humanities and related subjects. There is a growing interest in the community to build datasets capable of training machine learning models to extract relationships. However, annotating such datasets can be expensive and time-consuming, in addition to being limited to English. This paper applies guided distant supervision to create a large biographical relationship extraction dataset for German. Our dataset, composed of more than 80,000 instances for nine relationship types, is the largest biographical German relationship extraction dataset. We also create a manually annotated dataset with 2000 instances to evaluate the models and release it together with the dataset compiled using guided distant supervision. We train several state-of-the-art machine learning models on the automatically created dataset and release them as well. Furthermore, we experiment with multilingual and cross-lingual experiments that could benefit many low-resource languages.
Abstract (translated)
关系提取对于在数字人文学和相关领域提取和理解个人信息至关重要。随着社区对构建能够训练机器学习模型提取关系的数据集的兴趣不断增加，然而，为这类数据集进行标注往往代价昂贵且耗时，同时限制在英语范围内。本文应用指导远距离监督创建了一个用于德语的大型关系提取数据集。由九种关系类型组成的超过80,000个实例的数据集是最大的德语关系提取数据集。我们还创建了一个手动标注的数据集，包含2000个实例，用于评估模型并将其与使用指导远距离监督构建的数据集一起发布。我们在自动创建的数据集上训练了几个最先进的机器学习模型，并将其发布。此外，我们还尝试了多语言和跨语言实验，这些实验对许多低资源语言有利。
URL
https://arxiv.org/abs/2403.17143
