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Process Extraction from Natural Language Text

Public and private organizations always seek to achieve high standardization and improve performance of their business processes. Having control over business times, costs, errors and redundancy is vital to survive from continuous business revolutions [18, 31]. Business ProcessManagement is a discipline that aims to discover, analyze, and optimize business processes, typically represented in model diagrams. Unfortunately, the initial elicitation of a process model from documents is a time consuming and cost intensive operation, as argued in [15, 21]. Therefore, companies and the scientific community are interested in discovering novel algorithmic procedures to alleviate the initial creation of process models from documents. The extraction of a process model from documents is a complex task since the analysis of the natural language description of a process may produce multiple interpretation. This task is made up of three main activities. Filtering uninformative sentences of the process description out, because not all the sentences represent a process element. Then, the extraction of the process elements described in the text takes place. Finally, process elements discovered have to be logically organized following the semantic conveyed in the process description. So, defining the logical succession of process model elements is the last challenge to tackle. However, not only each sentence can describe multiple process elements, but also each word can have multiple meanings. To determine the correct intended meaning and to map it into the corresponding process element implies considering these two aspect at once. Also, there is the need to take care simultaneously of the multiple linguistics levels (syntactical, semantics and pragmatics) as well as ambiguities due to natural language. The common solution found in the literature to solve the problem of process extraction from natural language text relies on a two-steps transformation approach with intermediate representation. Here, the model is considered as a compound function in which the first function fa extracts process elements from a text and populates the structured intermediate representation, while the second function fb builds the process model from the intermediate representation. However, most contributions proposed in this area date back to several years ago and, hypothetically, they may be considered outdated, given the advances of Natural Language Processing (NLP) techniques in the last few years [22]. Maqbool et al. argue in [21] that current approaches may be not able to scale up to real world scenarios, highlighting the need of research in this direction. The limited data publicly available and the heterogeneous approaches proposed in the literature highlighted a lack of a fair comparative analysis among them by making hard