Identification of Decision Rules from Legislative Documents Using Machine Learning and Natural Language Processing

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dc.contributor.authorMichel, Maximilian
dc.contributor.authorDjurica, Djordje
dc.contributor.authorMendling, Jan
dc.date.accessioned2021-12-24T18:16:47Z
dc.date.available2021-12-24T18:16:47Z
dc.date.issued2022-01-04
dc.description.abstractDecision logic extraction from natural language texts can be a tedious, labor-intensive task. This is especially true for legislative texts, since they do not always follow usual speech and writing patterns. This paper explores the possibility of using machine learning and natural language processing approaches to identify decision rules within legislative documents, and ultimately provides the possibility of building an extraction algorithm on top of the solution to extract and visualize decision logic automatically. Such a novel method for decision rules identification bears the potential to reduce human labor, minimize mistakes, and lessen context dependency. To accomplish this, we use pre-trained word vectorization in conjunction with a complex multi-layer convolutional neural network (CNN). The relevant data used in this project was generated from the Austrian income tax code and labeled by hand. A quantitative evaluation shows that our approach can be trained on as little as a single code of law and still obtain significant accuracy.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.757
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/80097
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBusiness Rule Management Technologies
dc.subjectdecision logic
dc.subjectlegislative texts
dc.subjectmachine learning
dc.subjectnatural language processing
dc.titleIdentification of Decision Rules from Legislative Documents Using Machine Learning and Natural Language Processing
dc.type.dcmitext

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