The Effectiveness of Graph Contrastive Learning on Mathematical Information Retrieval

被引：0

作者：

Wang, Pei-Syuan ^{[1
]}

Chen, Hung-Hsuan ^{[1
]}

机构：

[1] Natl Cent Univ, Taoyuan, Taiwan

来源：

ADVANCES ON GRAPH-BASED APPROACHES IN INFORMATION RETRIEVAL, IRONGRAPHS 2024 | 2025年 / 2197卷

关键词：

Mathematical information retrieval; Graphical contrastive learning; Layout;

D O I：

10.1007/978-3-031-71382-8_5

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper details an empirical investigation into using Graph Contrastive Learning (GCL) to generate mathematical equation representations, a critical aspect of Mathematical Information Retrieval (MIR). Our findings reveal that this simple approach consistently exceeds the performance of the current leading formula retrieval model, TangentCFT. To support ongoing research and development in this field, we have made our source code accessible to the public at https://github. com/WangPeiSyuan/GCL-Formula- Retrieval/.

引用

页码：60 / 72

页数：13

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