Abstract: The present technology pertains to a method and system for assessing risks associated with facilities, based on using natural language processing. For example, a method can include receiving a natural language input comprising at least one raw text document associated with a facility and generating a plurality of segmented sentences from the raw text documents. The plurality of segmented sentences can be provided as inputs to a machine learning model trained to classify an input segmented sentence over a pre-defined lexicon of pharmaceutical terminology. Each segmented sentence can be classified into one or more classes given by the pre-defined lexicon of pharmaceutical terminology. A secondary classification can be performed for each classified segmented sentence to generate a production issue label based on an analysis of the classified segmented sentence. From the secondary classifications for the classified segmented sentences, at least one production category score for the facility can be generated.

Abstract: Disclosed are systems and methods for natural language processing. The method includes receiving, by a first processor, a natural language input including a raw text document and a desired output request, defining an ordered sequence of natural language tasks based at least in part on the natural language input, and generating a data structure including the raw text document. The first processor can then transmit, to a second processor, the data structure and a request to execute a first natural language task in the ordered sequence, and the second processor can execute the first natural language task based at least in part on the data structure to produce a first task output. The second processor can then insert the first task output into the data structure and transmit the data structure having the first task output to the first processor. The first processor can then generate an output based at least in part on the data structure and the desired output request.