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

TL;DR

This paper investigates whether mathematical formulae can be embedded like natural language using neural representation techniques, aiming to improve mathematical information retrieval.

Contribution

It introduces a novel 'symbol2vec' method for formula symbol embedding and a 'formula2vec' approach for MIR, exploring their potential in mathematical language processing.

Findings

Preliminary results show promising potential for formula embedding in MIR.

Analysis highlights key differences between natural and mathematical language.

Initial experiments indicate effectiveness of proposed embedding methods.

Abstract

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.