@inproceedings{db29e7eb51394ed2b3c06bc8f488c50f,
title = "The Effectiveness of Graph Contrastive Learning on Mathematical Information Retrieval",
abstract = "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/.",
keywords = "Graphical contrastive learning, Layout, Mathematical information retrieval",
author = "Wang, {Pei Syuan} and Chen, {Hung Hsuan}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 1st International Workshop on Graph-Based Approaches in Information Retrieval, IRonGRAPHS 2024 ; Conference date: 24-03-2024 Through 24-03-2024",
year = "2025",
doi = "10.1007/978-3-031-71382-8_5",
language = "???core.languages.en_GB???",
isbn = "9783031713811",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "60--72",
editor = "Ludovico Boratto and Mirko Marras and Giacomo Medda and Daniele Malitesta and Cataldo Musto and Erasmo Purificato",
booktitle = "Advances on Graph-Based Approaches in Information Retrieval - 1st International Workshop, IRonGraphs 2024, Proceedings",
}