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Preliminary Exploration of Formula Embedding for Mathematical Information Retrieval: can mathematical formulae be embedded like a natural language?

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arxiv 1707.05154 v2 pith:7AWHZMB4 submitted 2017-07-17 cs.IR

Liangcai Gao , Zhuoren Jiang , Yue Yin , Ke Yuan , Zuoyu Yan , Zhi Tang This is my paper · ORCID
classification cs.IR
keywords languagemathematicalformulanaturaltasksapplyingembeddinginformation
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While neural network approaches are achieving breakthrough performance in the natural language related fields, there have been few similar attempts at mathematical language related tasks. In this study, we explore the potential of applying neural representation techniques to Mathematical Information Retrieval (MIR) tasks. In more detail, we first briefly analyze the characteristic differences between natural language and mathematical language. Then we design a "symbol2vec" method to learn the vector representations of formula symbols (numbers, variables, operators, functions, etc.) Finally, we propose a "formula2vec" based MIR approach and evaluate its performance. Preliminary experiment results show that there is a promising potential for applying formula embedding models to mathematical language representation and MIR tasks.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Syntax Meets Semantics: Understanding Scientific Formulae

    cs.IR 2026-08 conditional novelty 6.0 of 10

    Formula syntax and textual semantics show weak direct correspondence but strong latent correlation; contrastive learning bridges the gap and lifts retrieval from ~5% to ~58% recall@10.

  2. E-Gen: Leveraging E-Graphs to Improve Continuous Representations of Symbolic Expressions

    cs.LG 2025-01 conditional novelty 6.0 of 10

    An e-graph-based data generator produces 55 million equivalent-expression training pairs, and embeddings trained on them beat prior math-embedding models and GPT-4o on several symbolic math tasks.

  3. SSEmb: A Joint Structural and Semantic Embedding Framework for Mathematical Formula Retrieval

    cs.IR 2025-08 conditional novelty 5.0 of 10

    SSEmb, which fuses Operator Graph contrastive embeddings with Sentence-BERT text embeddings, reports state-of-the-art contextualized formula retrieval on ARQMath-3.

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