Transformer-based approach to variable typing

The upsurge of multifarious endeavors across scientific fields propelled Big Data in the scientific domain. Despite the advancements in management systems, researchers find that mathematical knowledge remains one of the most challenging to manage due to the latter's inherent heterogeneity. One novel recourse being explored is variable typing where current works remain liminary and, thus, provide a wide room for contribution. In this study, a primordial attempt implement the end-to-end Entity Recognition (ER) and Relation Extraction (RE) approach variable typing was made using the BERT (Bidirectional Encoder Representations from Transformers) model. A micro-dataset was developed for this process. According to our findings, the model and RE model, respectively, have Precision of 0.8142 and 0.4919, Recall of 0.7816 0.6030, and F1-Scores of 0.7975 and 0.5418. Despite the limited dataset, the models performed par with values in the literature. This work also discusses the factors affecting this BERT-based