The Effectiveness of Graph Contrastive Learning on Mathematical Information Retrieval
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866910338887188480 |
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| author | Wang, Pei-Syuan Chen, Hung-Hsuan |
| author_facet | Wang, Pei-Syuan Chen, Hung-Hsuan |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13444 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Effectiveness of Graph Contrastive Learning on Mathematical Information Retrieval Wang, Pei-Syuan Chen, Hung-Hsuan Information Retrieval 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/. |
| title | The Effectiveness of Graph Contrastive Learning on Mathematical Information Retrieval |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2402.13444 |