Semantic Parsing of Mathematics by Context-based Learning from Aligned   Corpora and Theorem Proving
  We study methods for automated parsing of informal mathematical expressions into formal ones, a main prerequisite for deep computer understanding of informal mathematical texts. We propose a context-based parsing approach that combines efficient statistical learning of deep parse trees with their semantic pruning by type checking and large-theory automated theorem proving. We show that the methods very significantly improve on previous results in parsing theorems from the Flyspeck corpus. 
Cezary Kaliszyk, Josef Urban, Ji\v{r}\'i Vysko\v{c}il
cs.CL cs.AI