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We hypothesize that explicit integration of contextual information into anMulti-task Learning framework would emphasize the significance of context forboosting performance in jointly learning Named Entity Recognition (NER) andRelation Extraction (RE). Our work proves this hypothesis by segmentingentities from their surrounding context and by building contextualrepresentations using each independent segment. This relation representationallows for a joint NER/RE system that achieves near state-of-the-art (SOTA)performance on both NER and RE tasks while beating the SOTA RE system atend-to-end NER & RE with a 49.07 F1.

Jointly Learning Clinical Entities and Relations with Contextual Language Models and Explicit Context