mlsanityData to 14 Oct 2026

Symlink: A New Dataset for Scientific Symbol-Description Linking

arXiv 2204.12070 ↗First posted 26 Apr 2022cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 19 references that are in the map. First posted on arXiv 26 Apr 2022.

Builds on

7 of 19 references.

References · 7

  1. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,414
  2. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723
  3. 2019DocRED: A Large-Scale Document-Level Relation Extraction Datasetcited by 239
  4. 2016Image-to-Markup Generation with Coarse-to-Fine Attentioncited by 79
  5. 2021An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learningcited by 31
  6. 2021Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networkscited by 22
  7. 2018Improving the Representation and Conversion of Mathematical Formulae by Considering their Textual Contextcited by 6

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Citing papers per year: 2023: 2.

2 papers.

  1. 2023Envisioning the Next-Gen Document Readercited by 0
  2. 2023What Makes a Good Dataset for Symbol Description Reading?cited by 0