# Scientific Statement Classification over arXiv.org

arXiv: https://arxiv.org/abs/1908.10993 · HTML: https://mlsanity.com/paper/1908.10993/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Modeling Information Change in Science Communication with Semantically Matched Paraphrases](https://mlsanity.com/paper/2210.13001/) (2022)
- [Modular Multimodal Machine Learning for Extraction of Theorems and Proofs in Long Scientific Documents (Extended Version)](https://mlsanity.com/paper/2307.09047/) (2023)
- [ArGoT: A Glossary of Terms extracted from the arXiv](https://mlsanity.com/paper/2109.02801/) (2021)

## Abstract (arXiv)

We introduce a new classification task for scientific statements and release a large-scale dataset for supervised learning. Our resource is derived from a machine-readable representation of the arXiv.org collection of preprint articles. We explore fifty author-annotated categories and empirically motivate a task design of grouping 10.5 million annotated paragraphs into thirteen classes. We demonstrate that the task setup aligns with known success rates from the state of the art, peaking at a 0.91 F1-score via a BiLSTM encoder-decoder model. Additionally, we introduce a lexeme serialization for mathematical formulas, and observe that context-aware models could improve when also trained on the symbolic modality. Finally, we discuss the limitations of both data and task design, and outline potential directions towards increasingly complex models of scientific discourse, beyond isolated statements.