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In this paper, we present an end-to-end joint entity and relation extractionapproach based on transformer-based language models. We apply the model to thetask of linking mathematical symbols to their descriptions in LaTeX documents.In contrast to existing approaches, which perform entity and relationextraction in sequence, our system incorporates information from relationextraction into entity extraction. This means that the system can be trainedeven on data sets where only a subset of all valid entity spans is annotated.We provide an extensive evaluation of the proposed system and its strengths andweaknesses. Our approach, which can be scaled dynamically in computationalcomplexity at inference time, produces predictions with high precision andreaches 3rd place in the leaderboard of SemEval-2022 Task 12. For inputs in thedomain of physics and math, it achieves high relation extraction macro F1scores of 95.43% and 79.17%, respectively. The code used for training andevaluating our models is available at: https://github.com/nicpopovic/RE1st

AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First -- Using Relation Extraction to Identify Entities