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.

本研究旨在支持科学家们在研究数学公式中理解标识符的含义，通过对两种方法进行对比，我们发现使用基于词性标注的距离以及句子位置来计算标识符 - 定义概率的 Mathematical Language Processing 方法可以大大提高用户体验，工具提示提供了显示定义的悬停显示，显示出来的定义与实际标识符的含义大致匹配。