Inter-subdiscipline Analysis Based on Mathematical Statements

Rui Wang, Xiaoling Zhou, Jian Wu, Ou Wu

Abstract: A mathematical paper contains various mathematical statements, including definitions, theorems, lemmas, and so on. The mining of mathematical literature currently focuses on formulas and disregards statements. The present study investigates the (automatic) subdiscipline classification for mathematical statements. The classification results are applied into inter-subdiscipline analysis, including proportion and dependency analyses. First, a statement learning data is directly compiled from mathematical textbooks with a little human labeling to train an effective subdiscipline classifier. Second, a relatively large corpus, namely, analysis data, is compiled from mathematical journals. The classification results on the analysis data are subsequently used to quantify the inter-subdisciplinary relationships and conduct proportion analysis. Lastly, the dependency of different subdisciplines is analyzed and dependency chains among subdisciplines can be obtained.