HYBRID BATCH AND LIVE NATURAL LANGUAGE PROCESSING

摘要

A computer system performs live natural language processing (NLP) on data sources that are complex, remotely stored, and/or large, while satisfying restrictive time constraints. The computer system divides the NLP process into a batch NLP process and a live NLP process. The batch NLP process operates asynchronously over the relevant data set, which may be complex, remotely stored, and/or large, to summarize information into a summarized NLP data model. When the live NLP process is initiated, live NLP process receives as input the relevant information from the summarized NLP data model, possibly along with other data. The prior generation of the summarized NLP data model by the batch NLP process enables the live NLP process to perform NLP within time constraints that could not have been satisfied if the batch NLP process had not pre-processed the data set to produce the summarized NLP data model.