  Classic grammars and regular expressions can be used for a variety of
purposes, including parsing, intent detection, and matching. However, the
comparisons are performed at a structural level, with constituent elements
(words or characters) matched exactly. Recent advances in word embeddings show
that semantically related words share common features in a vector-space
representation, suggesting the possibility of a hybrid grammar and word
embedding. In this paper, we blend the structure of standard context-free
grammars with the semantic generalization capabilities of word embeddings to
create hybrid semantic grammars. These semantic grammars generalize the
specific terminals used by the programmer to other words and phrases with
related meanings, allowing the construction of compact grammars that match an
entire region of the vector space rather than matching specific elements.
