Natural Language Processing in Business Process Identification and Modeling

Abstract

Business Process Management (BPM) has been receiving increasing attention in recent years. Many organizations have been adapting their business to a process-centered view since they started noticing its potential to reduce costs, improve productivity and achieve higher levels of quality. However, implementing BPM in organizations requires time, making the automation of process identification and discovery highly desirable. To achieve this expectation, the application of Natural Language Processing (NLP) techniques and tools has emerged to generate process models from unstructured text. In this paper, we provide the results of a systematic literature review conducted in preparation and processing of natural language text aiming the extraction of business processes and process quality assurance. The study presents techniques applied to the BPM life-cycle phases of process identification, process discovery and process analysis as well as tools to support process discovery. This review covered papers from 2009 up to 2016 and identifies 518 articles of which 33 were selected as relevant to our work. The results of the present study may be valuable to support research in extraction of business process models from natural language text.

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