Semantic Parsing of Mathematics by Context-based Learning from Aligned Corpora and Theorem Proving.
arXiv: Computation and Language(2016)
摘要
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.
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