Integrating knowledge bases to improve coreference and bridging resolution for the chemical domain
Not on the map. Why?
Abstract (arXiv)
Resolving coreference and bridging relations in chemical patents is important for better understanding the precise chemical process, where chemical domain knowledge is very critical. We proposed an approach incorporating external knowledge into a multi-task learning model for both coreference and bridging resolution in the chemical domain. The results show that integrating external knowledge can benefit both chemical coreference and bridging resolution.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 16 Apr 2024.
Builds on
6 of 16 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,414
- 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 834
- 2019ScispaCy: Fast and Robust Models for Biomedical Natural Language Processingcited by 244
- 2018Higher-order Coreference Resolution with Coarse-to-fine Inferencecited by 150
- 2020Multi-task Learning Based Neural Bridging Reference Resolutioncited by 7
Built on by
Not cited yet.