1. A method of unsupervised learning of deep patterns for semantic parsing, using an algorithm that operates over a VerbNet corpus, the method comprising: receiving a set of exemplary sentences, including a first exemplary sentence, from a language net; extracting, from VerbNet, a plurality of markup language files corresponding to a usage pattern P; parsing each of the exemplary sentences to yield a set of training constituency trees respectively corresponding to the exemplary sentences; performing maximal frequent subtree analysis on the set of training constituency trees to yield an unfiltered set of deep pattern trees; and filtering at least one irrelevant tree(s) from the set of deep pattern trees to obtain a filtered set of deep pattern trees, with the at least one irrelevant tree(s) do not contain a part of speech that is included in the usage pattern P; deleting redundant leaves from at least one of the following: the filtered set of deep pattern, and/or the unfiltered set of deep pattern trees; and matching nodes of each tree pattern, of the filtered set of deep tree patterns, with items of the usage pattern P using machine logic based rules to facilitate translation of the filtered set of deep pattern trees into annotation query language rules; At least the parsing step is performed by computer software running on computer hardware.