TL;DR
This paper advances coreference resolution in scientific research papers by creating a new annotated corpus, applying transfer learning, and demonstrating significant improvements in knowledge graph population across multiple disciplines.
Contribution
It introduces a new annotated corpus for scientific papers, applies transfer learning for coreference resolution, and shows improved knowledge graph population results.
Findings
Transfer learning outperforms baselines with F1 of 61.4
Coreference resolution significantly improves KG quality
Annotated corpus covers 10 scientific disciplines
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
