From Natural Language Text to Visual Models: A survey of Issues and Approaches

Cristina-Claudia Osman () and Paula-Georgiana Zalhan ()

Informatica Economica, 2016, vol. 20, issue 4, 44-61

Abstract: Over the last 20 years, research groups focused on automating the process of extracting valuable information from Natural Language text in order to discover data and process models. In this context, several tools and approaches have been proposed. The overall objective of this survey is to examine existing literature works that transform textual specifications into visual models. This paper aims to give a comprehensive account of the existing tools meant to discover data and process models from natural language text. Our analysis focuses on approaches of these tools in the model extraction process and highlight issues of each proposed approach. In the case of object oriented software modelling of data models extraction we analyze the degree of automation, efficiency and completeness of the transformation process. Regarding process models extraction, the study is not limited only to business process discovery, but it also provides case studies from several fields such as medical or archaeological. Even if not all the tools developed are clearly depicting a Natural Language Processing technique, a review of each approach is presented.

Keywords: Natural Language Processing; Data Model; Business Process Model (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:aes:infoec:v:20:y:2016:i:4:p:44-61

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