Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language
This work addresses the lack of annotated datasets for non-English languages in relation extraction, particularly benefiting digital humanities and low-resource languages, though it is incremental as it adapts an existing method to a new language.
The paper tackles the problem of expensive and time-consuming annotation for multilingual relation extraction by applying guided distant supervision to create a large biographical dataset for German, resulting in over 80,000 instances for nine relationship types, which is the largest such dataset for German, and training state-of-the-art models on it.
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.