Mathematical symbols and descriptions appear in various forms across document section boundaries without explicit markup. In this paper, we present a new large-scale dataset that emphasizes extracting symbols and descriptions in scientific documents. Symlink annotates scientific papers of 5 different domains (i.e., computer science, biology, physics, mathematics, and economics). Our experiments on Symlink demonstrate the challenges of the symbol-description linking task for existing models and call for further research effort in this area. We will publicly release Symlink to facilitate future research.
@article{arxiv.2204.12070,
title = {Symlink: A New Dataset for Scientific Symbol-Description Linking},
author = {Viet Dac Lai and Amir Pouran Ben Veyseh and Franck Dernoncourt and Thien Huu Nguyen},
journal= {arXiv preprint arXiv:2204.12070},
year = {2022}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2202.09695