1. A method comprising: receiving, by a processing device, data associated with a domain input; determining, by the processing device, statistical characteristics associated with one or more features of the domain input to generate feature statistical characteristics; identifying, by the processing device and based on the generated feature statistical characteristics, a plurality of learned models, each of the plurality of learned models being associated with a respective other domain input, each respective other domain input being different than the domain input; comparing, by the processing device, the generated feature statistical characteristics with respective statistical characteristics associated with one or more features of the plurality of learned models; identifying, by the processing device and based on the comparing, at least one learned model of the plurality of learned models that satisfies a threshold value; modifying, by the processing device, the identified at least one learned model to create at least one trained model, the at least one learned model being modified by analyzing the respective statistical characteristics associated with the one or more features of the identified at least one learned model and modifying a portion of the respective statistical characteristics associated with the one or more features of the identified at least one learned model that corresponds to the generated feature statistical characteristics associated with the domain input, and the at least one trained model being different than each of the at least one learned model; and using, by the processing device, the at least one trained model as a domain model to process the received data.
1. A method comprising: receiving, by a processing device, data associated with a domain input; determining, by the processing device, statistical characteristics associated with one or more features of the domain input to generate feature statistical characteristics; identifying, by the processing device and based on the generated feature statistical characteristics, a plurality of learned models, each of the plurality of learned models being associated with a respective other domain input, each respective other domain input being different than the domain input; comparing, by the processing device, the generated feature statistical characteristics with respective statistical characteristics associated with one or more features of the plurality of learned models; identifying, by the processing device and based on the comparing, at least one learned model of the plurality of learned models that satisfies a threshold value; modifying, by the processing device, the identified at least one learned model to create at least one trained model, the at least one learned model being modified by analyzing the respective statistical characteristics associated with the one or more features of the identified at least one learned model and modifying a portion of the respective statistical characteristics associated with the one or more features of the identified at least one learned model that corresponds to the generated feature statistical characteristics associated with the domain input, and the at least one trained model being different than each of the at least one learned model; and using, by the processing device, the at least one trained model as a domain model to process the received data. 7. The method of claim 1 , where the domain input comprises text.