  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.
