AutoMSC: Automatic Assignment of Mathematics Subject Classification Labels
This work addresses the need for efficient classification in digital libraries and reviewing services in mathematics, representing an incremental improvement over manual methods.
The paper tackled the problem of automatically assigning coarse-grained Mathematics Subject Classification (MSC) labels to research papers, achieving an F1-score of over 77%, which is close to the 81% agreement between human experts, and reduced manual effort by 86% while maintaining 81% precision.
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.