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

Henrik Leopold (Corresponding author), Han van der Aa, Jelmer Offenberg, Hajo A. Reijers

    Research output: Contribution to journalArticleAcademicpeer-review

    8 Citations (Scopus)
    4 Downloads (Pure)

    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.

    Original languageEnglish
    Pages (from-to)30-39
    Number of pages10
    JournalInformation Systems
    Volume83
    DOIs
    Publication statusPublished - 1 Jul 2019

    Keywords

    • Hidden Markov models
    • Label analysis
    • Natural language
    • Process model

    Fingerprint

    Dive into the research topics of 'Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels'. Together they form a unique fingerprint.

    Cite this