Scientific Statement Classification over arXiv.org

TL;DR

This paper presents a new classification task for scientific statements using a large dataset from arXiv.org, achieving high accuracy with neural models and exploring the integration of mathematical formulas.

Contribution

It introduces a novel dataset and task for classifying scientific statements, along with a method for handling mathematical formulas and insights into model performance.

Findings

Achieved up to 0.91 F1-score with a BiLSTM model.

Developed a lexeme serialization for formulas.

Discussed limitations and future directions for scientific discourse modeling.

Abstract

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.