Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

Yi Luan, Luheng He, Mari Ostendorf, Hannaneh Hajishirzi


Abstract
We introduce a multi-task setup of identifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called SciIE with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.
Anthology ID:
D18-1360
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3219–3232
Language:
URL:
https://aclanthology.org/D18-1360
DOI:
10.18653/v1/D18-1360
Bibkey:
Cite (ACL):
Yi Luan, Luheng He, Mari Ostendorf, and Hannaneh Hajishirzi. 2018. Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3219–3232, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (Luan et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1360.pdf
Video:
 https://vimeo.com/306113930
Code
 additional community code
Data
SciERCSemantic Scholar