1. A system comprising: a computer-readable memory storing executable instructions; and one or more processors in communication with the computer-readable memory, wherein the one or more processors are programmed by the executable instructions to at least: obtain usage data regarding prior usage, by a user, of a natural language processing system; train, based at least partly on the usage data, a personal model for use in named entity recognition, the personal model comprising a conditional random field model; interpolate the personal model and a general model to obtain a composite model, the general model comprising a conditional random field model, wherein interpolation of the personal model and the general model comprises interpolation of an element of the personal model with a corresponding element of the general model; receive audio data regarding an utterance of the user; generate a transcription of the utterance using automatic speech recognition; process, based at least partly on the composite model, the transcription with a named entity recognition module to determine at least one named entity from the transcription; and generate a response based at least partly on the at least one named entity.
1. A system comprising: a computer-readable memory storing executable instructions; and one or more processors in communication with the computer-readable memory, wherein the one or more processors are programmed by the executable instructions to at least: obtain usage data regarding prior usage, by a user, of a natural language processing system; train, based at least partly on the usage data, a personal model for use in named entity recognition, the personal model comprising a conditional random field model; interpolate the personal model and a general model to obtain a composite model, the general model comprising a conditional random field model, wherein interpolation of the personal model and the general model comprises interpolation of an element of the personal model with a corresponding element of the general model; receive audio data regarding an utterance of the user; generate a transcription of the utterance using automatic speech recognition; process, based at least partly on the composite model, the transcription with a named entity recognition module to determine at least one named entity from the transcription; and generate a response based at least partly on the at least one named entity. 2. The system of claim 1 , wherein the element of the personal model comprises a first parameter vector, the corresponding element of the general model comprises a second parameter vector, and interpolation of the element of the personal model with the corresponding element of the general model comprises performing a weighted average of the first parameter vector and the second parameter vector.