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http://hdl.handle.net/1942/32589
Title: | Natural language techniques supporting decision modelers | Authors: | Arco, Leticia NAPOLES RUIZ, Gonzalo VANHOENSHOVEN, Frank Lara, Ana Laura Casas, Gladys VANHOOF, Koen |
Issue Date: | 2021 | Publisher: | SPRINGER | Source: | Data mining and knowledge discovery, 35(1), p. 290-320 | Abstract: | Decision Model and Notation (DMN) has become a relevant topic for organizations since it allows users to control their processes and organizational decisions. The increasing use of DMN decision tables to capture critical business knowledge raises the need for supporting analysis tasks such as the extraction of inputs, outputs and their relations from natural language descriptions. In this paper, we create a stepping stone towards implementing a Natural Language Processing framework to model decisions based on the DMN standard. Our proposal contributes to the generation of decision rules and tables from a single sentence analysis. This framework comprises three phases: (1) discourse and semantic analysis, (2) syntactic analysis and (3) decision table construction. To the best of our knowledge, this is the first attempt devoted to automatically discovering decision rules according to the DMN terminology from natural language descriptions. Aiming at assessing the quality of the resultant decision tables, we have conducted a survey involving 16 DMN 2 Leticia Arco et al. experts. The results have shown that our framework is able to generate semantically correct tables. It is convenient to mention that our proposal does not aim to replace analysts but support them in creating better models with less effort. | Keywords: | Decision Modeling and Notation;Decision rules;Decision tables;Natural Language Processing | Document URI: | http://hdl.handle.net/1942/32589 | ISSN: | 1384-5810 | e-ISSN: | 1573-756X | DOI: | 10.1007/s10618-020-00718-4 | ISI #: | WOS:000587086800001 | Rights: | © The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2020. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2021 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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manuscript.pdf | Peer-reviewed author version | 1.24 MB | Adobe PDF | View/Open |
s10618-020-00718-4.pdf Restricted Access | Published version | 1.64 MB | Adobe PDF | View/Open Request a copy |
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