18. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause a computer to perform functions that include: reading text data corresponding to one or more messages, wherein the text data comprises at least one of natural language content and metadata; 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 a trained statistical language model, one or more predictive labels as annotations corresponding to language patterns associated with the text data, and wherein the language patterns comprise at least one of part-of-speech, syntactic role, and sentiment 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; and performing, based on information from the message store and associated with the one or more messages, global analytics functions that include: identifying an annotation error in the created semantic annotations, updating the respective semantic annotation to correct the annotation error, and back-propagating the updated semantic annotation into training data for further language model training; wherein aggregating the one or more annotated messages and storing the information associated with the aggregated one or more annotated messages comprises constructing a global knowledge graph representation corresponding to the aggregated one or more annotated messages, and wherein identifying the annotation error, updating the respective semantic annotation to correct the annotation error, and back-propagating the updated semantic annotation comprises: (a) identifying the annotation error from the knowledge graph representation, (b) updating the respective semantic annotation in the knowledge graph representation to correct the annotation error, (c) back-propagating the updated semantic annotation into the training data for the further language model training, and (d) 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.
18. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause a computer to perform functions that include: reading text data corresponding to one or more messages, wherein the text data comprises at least one of natural language content and metadata; 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 a trained statistical language model, one or more predictive labels as annotations corresponding to language patterns associated with the text data, and wherein the language patterns comprise at least one of part-of-speech, syntactic role, and sentiment 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; and performing, based on information from the message store and associated with the one or more messages, global analytics functions that include: identifying an annotation error in the created semantic annotations, updating the respective semantic annotation to correct the annotation error, and back-propagating the updated semantic annotation into training data for further language model training; wherein aggregating the one or more annotated messages and storing the information associated with the aggregated one or more annotated messages comprises constructing a global knowledge graph representation corresponding to the aggregated one or more annotated messages, and wherein identifying the annotation error, updating the respective semantic annotation to correct the annotation error, and back-propagating the updated semantic annotation comprises: (a) identifying the annotation error from the knowledge graph representation, (b) updating the respective semantic annotation in the knowledge graph representation to correct the annotation error, (c) back-propagating the updated semantic annotation into the training data for the further language model training, and (d) 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. 19. The computer-readable medium of claim 18 , wherein identifying the annotation error from the knowledge graph representation and updating the respective annotation comprises receiving an annotation correction from at least one user via a user interface.