Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language
<|reference_start|>Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language: 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.<|reference_end|>
@article{plum2024guided,
    title={Guided Distant Supervision for Multilingual Relation Extraction Data:
  Adapting to a New Language},
    author={Alistair Plum, Tharindu Ranasinghe, Christoph Purschke},
    journal={arXiv preprint arXiv:2403.17143},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17143},
    primaryClass={cs.CL cs.LG}
}