1. A computer-implemented method, comprising: reading text data corresponding to one or more messages; creating one or more semantic annotations to the text data to generate one or more annotated messages, wherein creating the one or more semantic annotations comprises generating, at least in part by at least one trained statistical language model trained by machine learning, one or more predictive labels as annotations corresponding to language patterns associated with the text data; aggregating the one or more annotated messages and storing information associated with the aggregated one or more annotated messages in a message store, wherein the aggregating comprises constructing a global knowledge representation associated with the aggregated one or more annotated messages; and performing an automated process, based at least in part on the global knowledge representation, the automated process comprising one or more global analytics functions that include: (a) identifying an annotation error in the created one or more semantic annotations, (b) automatically updating the respective semantic annotation in the global knowledge representation to correct the annotation error, to form an updated semantic annotation, and (c) back-propagating the updated semantic annotation into training data for further language model training by machine learning, wherein the back-propagating comprises forming updated training data, including the updated semantic annotation, for additional training of the at least one statistical language model, wherein the additional training comprises training the at least one statistical language model to perform one or more local analytics functions and to generate predictive labels for additional semantic annotations to text data; and performing steps (a)-(c) repeatedly until a predetermined level of accuracy of the annotations has been reached or a predetermined number of iterations have been performed.