AutoMSC: Automatic Assignment of Mathematics Subject Classification Labels

TL;DR

This paper presents a machine learning approach for automatically assigning coarse-grained Mathematics Subject Classification labels to research papers, achieving high accuracy and reducing manual effort significantly.

Contribution

It introduces a novel automated classification method for MSC labels, closely matching expert agreement and significantly decreasing manual workload.

Findings

Achieves over 77% F1-score in classification accuracy.

Reduces manual classification effort by 86%.

Maintains 81% precision in automatic labeling.

Abstract

Authors of research papers in the fields of mathematics, and other math-heavy disciplines commonly employ the Mathematics Subject Classification (MSC) scheme to search for relevant literature. The MSC is a hierarchical alphanumerical classification scheme that allows librarians to specify one or multiple codes for publications. Digital Libraries in Mathematics, as well as reviewing services, such as zbMATH and Mathematical Reviews (MR) rely on these MSC labels in their workflows to organize the abstracting and reviewing process. Especially, the coarse-grained classification determines the subject editor who is responsible for the actual reviewing process. In this paper, we investigate the feasibility of automatically assigning a coarse-grained primary classification using the MSC scheme, by regarding the problem as a multi-class classification machine learning task. We find that our method achieves an (F_1)-score of over 77%, which is remarkably close to the agreement of zbMATH and MR ((F_1)-score of 81%). Moreover, we find that the method's confidence score allows for reducing the effort by 86% compared to the manual coarse-grained classification effort while maintaining a precision of 81% for automatically classified articles.