Guided Distant Supervision for Multilingual Relation Extraction Data : Adapting to a New Language

Plum, Alistair and Ranasinghe, Tharindu and Purschke, Christoph (2024) Guided Distant Supervision for Multilingual Relation Extraction Data : Adapting to a New Language. In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) :. ELRA and ICCL, ITA, pp. 7982-7992. ISBN 9782493814104

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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 zero-shot experiments that could benefit many low-resource languages.