1. Coreference Resolution in Research Papers from Multiple Domains
- Author
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Ralph Ewerth, Daniel Uwe Müller, Anett Hoppe, and Arthur Brack
- Subjects
0301 basic medicine ,education.field_of_study ,Coreference ,Computer science ,business.industry ,Population ,02 engineering and technology ,Resolution (logic) ,computer.software_genre ,Task (project management) ,03 medical and health sciences ,Information extraction ,030104 developmental biology ,0202 electrical engineering, electronic engineering, information engineering ,Question answering ,020201 artificial intelligence & image processing ,Artificial intelligence ,education ,F1 score ,Transfer of learning ,business ,computer ,Natural language processing - Abstract
Coreference resolution is essential for automatic text understanding to facilitate high-level information retrieval tasks such as text summarisation or question answering. Previous work indicates that the performance of state-of-the-art approaches (e.g. based on BERT) noticeably declines when applied to scientific papers. In this paper, we investigate the task of coreference resolution in research papers and subsequent knowledge graph population. We present the following contributions: (1) We annotate a corpus for coreference resolution that comprises 10 different scientific disciplines from Science, Technology, and Medicine (STM); (2) We propose transfer learning for automatic coreference resolution in research papers; (3) We analyse the impact of coreference resolution on knowledge graph (KG) population; (4) We release a research KG that is automatically populated from 55,485 papers in 10 STM domains. Comprehensive experiments show the usefulness of the proposed approach. Our transfer learning approach considerably outperforms state-of-the-art baselines on our corpus with an F1 score of 61.4 (+11.0), while the evaluation against a gold standard KG shows that coreference resolution improves the quality of the populated KG significantly with an F1 score of 63.5 (+21.8).
- Published
- 2021
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