9. A virtual personal assistant (VPA) server for automatically querying a database for contextual natural language processing, the VPA server comprising: a speech recognition engine to (i) receive, from a computing device, audio input data representing a request spoken by a user of the computing device and (ii) produce a textual representation of the user request based on the audio input data, the textual representation including a plurality of words; a metadata interpretation module to: receive a plurality of language models and associated database query mappings from the computing device; and index the plurality of language models to determine a plurality of important words of each of the plurality of language models; and a request interpretation module to: determine, for each of the plurality of language models, a relevance measure of a plurality of words of the textual representation of the user request based on the plurality of important words of the corresponding language model; generate a ranking of the determined relevance measures corresponding to the plurality of language models; generate, based on the ranking of the determined relevance measures, a semantic representation of the textual representation; generate a database query as a function of the semantic representation using a database query mapping associated with a first language model of the plurality of language models, the first language model associated with a word of the textual representation; apply the database query to a context database to generate query results; and transmit the query results from the VPA server to the computing device.
10. The VPA server of claim 9 , wherein the request interpretation module is further to: generate a plurality of interpretations of the textual representation, wherein each interpretation comprises a plurality of word groups selected from the words of the textual representation; generate, for each word group of each interpretation, a ranking of each of the plurality of language models that orders the plurality of language models based on relevancy to the word group based on the plurality of important words of the corresponding language model; and generate a composite ranking for each interpretation based on the rankings of the word groups of the associated interpretation, wherein to generate the semantic representation comprises to generate the semantic representation as a function of the composite rankings of the interpretations.