Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels

Leopold, Henrik ; Aa, Han van der ; Offenberg, Jelmer ; Reijers, Hajo A.

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Document Type: Article
Year of publication: 2019
The title of a journal, publication series: Information Systems : IS
Volume: 83
Page range: 30-39
Place of publication: Amsterdam
Publishing house: Elsevier
ISSN: 0094-453X , 0306-4379
Publication language: English
Institution: School of Business Informatics and Mathematics > Artificial Intelligence Methods (Juniorprofessur) (van der Aa 2020-)
Subject: 004 Computer science, internet
Abstract: Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models’ activity labels. Since these labels are typically short and diverse in terms of their grammatical style, standard natural language processing tools are not suitable to analyze them. While a dedicated technique for the analysis of process model activity labels was proposed in the past, it suffers from considerable limitations. First of all, its performance varies greatly among data sets with different characteristics and it cannot handle uncommon grammatical styles. What is more, adapting the technique requires in-depth domain knowledge. We use this paper to propose a machine learning-based technique for activity label analysis that overcomes the issues associated with this rule-based state of the art. Our technique conceptualizes activity label analysis as a tagging task based on a Hidden Markov Model. By doing so, the analysis of activity labels no longer requires the manual specification of rules. An evaluation using a collection of 15,000 activity labels demonstrates that our machine learning-based technique outperforms the state of the art in all aspects. Previous article in issue

Dieser Datensatz wurde nicht während einer Tätigkeit an der Universität Mannheim veröffentlicht, dies ist eine Externe Publikation.

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