Please use this identifier to cite or link to this item: doi:10.22028/D291-33398
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Title: Supporting Business Process Modeling Using RNNs for Label Classification
Author(s): Hake, Philip
Zapp, Manuel
Fettke, Peter
Loos, Peter
Editor(s): Frasincar, Flavius
Ittoo, Ashwin
Nguyen, Le Minh
Métais, Elisabeth
Language: English
Title: Natural language processing and information systems : 22nd International Conference on Applications of Natural Language to Information Systems, NLDB 2017, Liège, Belgium, June 21-23, 2017 : proceedings
Startpage: 283
Endpage: 286
Publisher/Platform: Springer
Year of Publication: 2017
Place of publication: Cham
Title of the Conference: NLDB 2017
Place of the conference: Liège, Belgium
Publikation type: Conference Paper
Abstract: Business Process Models describe the activities of a company in an abstracted manner. Typically, the labeled nodes of a process model contain only sparse textual information. The presented approach uses an LSTM network to classify the labels contained in a business process model. We first apply a Word2Vec algorithm to the words contained in the labels. Afterwards, we feed the resulting data into our LSTM network. We train and evaluate our models on a corpus consisting of more than 24,000 labels of business process models. Using our trained classification model, we are able to distinguish different constructs of a process modeling language based on their label. Our experimental evaluation yields an accuracy of 95.71% on the proposed datasets.
DOI of the first publication: 10.1007/978-3-319-59569-6_35
URL of the first publication:
Link to this record: hdl:20.500.11880/30717
ISBN: 978-3-319-59569-6
Date of registration: 23-Feb-2021
Notes: Lecture notes in computer science ; 10260
Faculty: HW - Fakultät für Empirische Humanwissenschaften und Wirtschaftswissenschaft
Department: HW - Wirtschaftswissenschaft
Professorship: HW - Prof. Dr. Peter Loos
Collections:Die Universitätsbibliographie

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