TY  - JOUR
A1  - Leopold, Henrik
A1  - van der Aa, Han
A1  - Offenberg, Jelmer
A1  - Reijers, Hajo A.
T1  - Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels
JF  - Information systems
N2  - 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.
KW  - Label analysis
KW  - Process model
KW  - Natural language
KW  - Hidden Markov models
Y1  - 2019
U6  - https://doi.org/10.1016/j.is.2019.02.005
SN  - 0306-4379
SN  - 1873-6076
VL  - 83
SP  - 30
EP  - 39
PB  - Elsevier
CY  - Oxford
ER  - 
TY  - GEN
A1  - Han van der, Aa
A1  - Di Ciccio, Claudio
A1  - Leopold, Henrik
A1  - Reijers, Hajo A.
T1  - Extracting Declarative Process Models from Natural Language
T2  - Advanced Information Systems Engineering (CAISE 2019)
N2  - Process models are an important means to capture information on organizational operations and often represent the starting point for process analysis and improvement. Since the manual elicitation and creation of process models is a time-intensive endeavor, a variety of techniques have been developed that automatically derive process models from textual process descriptions. However, these techniques, so far, only focus on the extraction of traditional, imperative process models. The extraction of declarative process models, which allow to effectively capture complex process behavior in a compact fashion, has not been addressed. In this paper we close this gap by presenting the first automated approach for the extraction of declarative process models from natural language. To achieve this, we developed tailored Natural Language Processing techniques that identify activities and their inter-relations from textual constraint descriptions. A quantitative evaluation shows that our approach is able to generate constraints that closely resemble those established by humans. Therefore, our approach provides automated support for an otherwise tedious and complex manual endeavor.
KW  - Declarative modelling
KW  - Natural language processing
KW  - Model extraction
Y1  - 2019
SN  - 978-3-030-21290-2
SN  - 978-3-030-21289-6
U6  - https://doi.org/10.1007/978-3-030-21290-2_23
SN  - 0302-9743
SN  - 1611-3349
VL  - 11483
SP  - 365
EP  - 382
PB  - Springer
CY  - Cham
ER  -