Applied Sciences (Apr 2022)

PaperNet: A Dataset and Benchmark for Fine-Grained Paper Classification

  • Tan Yue,
  • Yong Li,
  • Xuzhao Shi,
  • Jiedong Qin,
  • Zijiao Fan,
  • Zonghai Hu

DOI
https://doi.org/10.3390/app12094554
Journal volume & issue
Vol. 12, no. 9
p. 4554

Abstract

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Document classification is an important area in Natural Language Processing (NLP). Because a huge amount of scientific papers have been published at an accelerating rate, it is beneficial to carry out intelligent paper classifications, especially fine-grained classification for researchers. However, a public scientific paper dataset for fine-grained classification is still lacking, so the existing document classification methods have not been put to the test. To fill this vacancy, we designed and collected the PaperNet-Dataset that consists of multi-modal data (texts and figures). PaperNet 1.0 version contains hierarchical categories of papers in the fields of computer vision (CV) and NLP, 2 coarse-grained and 20 fine-grained (7 in CV and 13 in NLP). We ran current mainstream models on the PaperNet-Dataset, along with a multi-modal method that we propose. Interestingly, none of these methods reaches an accuracy of 80% in fine-grained classification, showing plenty of room for improvement. We hope that PaperNet-Dataset will inspire more work in this challenging area.

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