AutoMSC：数学学科分类标签的自动分配
AutoMSC: Automatic Assignment of Mathematics Subject Classification Labels
摘要：
数学领域和其他数学密集学科的研究论文的作者通常使用数学主题分类（MSC）方案来搜索相关文献。MSC是一个层次化的图书分类方案，允许图书馆员为出版物指定一个或多个代码。数学领域的数字图书馆以及zbMATH和Mathematical Reviews（MR）等评审服务在其工作流程中依赖这些MSC标签来组织摘要和评审过程。特别是，粗粒度的分类决定了负责实际审查过程的主题编辑。在本文中，我们调查的可行性，自动分配粗粒度的主要分类使用MSC计划，把这个问题作为一个多类分类机器学习任务。我们发现，我们的方法实现了超过77%的（F_1）-分数，这是非常接近的协议zbMATH和MR（（F_1）-分数81%）。此外，我们发现，该方法的置信度分数允许减少86%的努力相比，手动粗粒度分类的努力，同时保持81%的精度为自动分类的文章。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
