Integrating knowledge bases to improve coreference and bridging resolution for the chemical domain
This work addresses the need for better understanding of chemical processes in patents, but it is incremental as it applies existing multi-task learning with knowledge integration to a specific domain.
The paper tackled the problem of resolving coreference and bridging relations in chemical patents by proposing an approach that integrates external knowledge into a multi-task learning model, resulting in improved performance for both tasks.
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.