Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911815745667072 |
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| author | Plum, Alistair Ranasinghe, Tharindu Purschke, Christoph |
| author_facet | Plum, Alistair Ranasinghe, Tharindu Purschke, Christoph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17143 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language Plum, Alistair Ranasinghe, Tharindu Purschke, Christoph Computation and Language Machine Learning 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. |
| title | Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2403.17143 |