  In natural language, words and phrases themselves imply the semantics. In
contrast, the meaning of identifiers in mathematical formulae is undefined.
Thus scientists must study the context to decode the meaning. The Mathematical
Language Processing (MLP) project aims to support that process. In this paper,
we compare two approaches to discover identifier-definition tuples. At first we
use a simple pattern matching approach. Second, we present the MLP approach
that uses part-of-speech tag based distances as well as sentence positions to
calculate identifier-definition probabilities. The evaluation of our
prototypical system, applied on the Wikipedia text corpus, shows that our
approach augments the user experience substantially. While hovering the
identifiers in the formula, tool-tips with the most probable definitions occur.
Tests with random samples show that the displayed definitions provide a good
match with the actual meaning of the identifiers.
