Scientific Statement Classification over arXiv.org
This work addresses the need for automated classification of scientific discourse for researchers and AI systems, though it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of classifying scientific statements by creating a large-scale dataset from arXiv.org preprints and grouping 10.5 million paragraphs into thirteen classes, achieving a peak F1-score of 0.91 with a BiLSTM encoder-decoder model.
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.