Semantically Orthogonal Framework for Citation Classification: Disentangling Intent and Content

TL;DR

This paper introduces SOFT, a framework that separates citation intent from cited content type, improving classification accuracy and consistency across disciplines in scholarly datasets.

Contribution

The paper presents SOFT, a novel semantic framework that disentangles citation intent and content type, enhancing annotation reliability and cross-domain applicability.

Findings

SOFT improves agreement between human and AI annotations.

Enhanced classification performance with zero-shot and fine-tuned models.

Better cross-domain generalization over previous frameworks.

Abstract

Understanding the role of citations is essential for research assessment and citation-aware digital libraries. However, existing citation classification frameworks often conflate citation intent (why a work is cited) with cited content type (what part is cited), limiting their effectiveness in auto classification due to a dilemma between fine-grained type distinctions and practical classification reliability. We introduce SOFT, a Semantically Orthogonal Framework with Two dimensions that explicitly separates citation intent from cited content type, drawing inspiration from semantic role theory. We systematically re-annotate the ACL-ARC dataset using SOFT and release a cross-disciplinary test set sampled from ACT2. Evaluation with both zero-shot and fine-tuned Large Language Models demonstrates that SOFT enables higher agreement between human annotators and LLMs, and supports stronger classification performance and robust cross-domain generalization compared to ACL-ARC and SciCite annotation frameworks. These results confirm SOFT's value as a clear, reusable annotation standard, improving clarity, consistency, and generalizability for digital libraries and scholarly communication infrastructures. All code and data are publicly available on GitHub https://github.com/zhiyintan/SOFT.