7. A computer system for unsupervised learning of deep patterns for semantic parsing, using an algorithm that operates over a VerbNet corpus, the computer system comprising: a processor(s) set; and a software storage device; wherein: the processor set is structured, located, connected and/or programmed to run software stored on the software storage device; and the software comprises: first program instructions programmed to extract, from VerbNet, a plurality of markup language files corresponding to a usage pattern P; second program instructions programmed to parse each of the exemplary sentences to yield a set of training constituency trees respectively corresponding to the exemplary sentences; third program instructions programmed to perform maximal frequent subtree analysis on the set of training constituency trees to yield an unfiltered set of deep pattern trees; and fourth program instructions programmed to filter 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; fifth program instructions programmed to delete 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 sixth program instructions programmed to match the 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 (AQL) rules.
7. A computer system for unsupervised learning of deep patterns for semantic parsing, using an algorithm that operates over a VerbNet corpus, the computer system comprising: a processor(s) set; and a software storage device; wherein: the processor set is structured, located, connected and/or programmed to run software stored on the software storage device; and the software comprises: first program instructions programmed to extract, from VerbNet, a plurality of markup language files corresponding to a usage pattern P; second program instructions programmed to parse each of the exemplary sentences to yield a set of training constituency trees respectively corresponding to the exemplary sentences; third program instructions programmed to perform maximal frequent subtree analysis on the set of training constituency trees to yield an unfiltered set of deep pattern trees; and fourth program instructions programmed to filter 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; fifth program instructions programmed to delete 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 sixth program instructions programmed to match the 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 (AQL) rules. 9. The system of claim 7 wherein: the language net organizes the exemplary sentences into a set of usage patterns; and the third program instructions are further programmed to perform maximal subtree analysis separately for each sub-set of training constituency trees respectively corresponding to each usage pattern in the language net.