  While there exist many methods in machine learning for comparison of letter
string data, most are better equipped to handle strings that represent natural
language, and their performance will not hold up when presented with strings
that correspond to mathematical expressions. Based on the graphical
representation of the expression tree, here we propose a simple method for
encoding such expressions that is only sensitive to their structural
properties, and invariant to the specifics which can vary between two seemingly
different, but semantically similar mathematical expressions.
