  In problem solving, understanding the problem that one seeks to solve is an
essential initial step. In this paper, we propose computational methods for
facilitating problem understanding through the task of recognizing the unknown
in specifications of long Math problems. We focus on the topic of Probability.
Our experimental results show that learning models yield strong results on the
task, a promising first step towards human interpretable, modular approaches to
understanding long Math problems.
