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Extracting Decision Dependencies and Decision Logic from Text Using Deep Learning Techniques

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Business Process Management Workshops (BPM 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 436))

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Abstract

Decision models are increasingly being used in modeling business processes. Hence, extracting decision models automatically from texts would help decision modellers by reducing modeling time and supporting them in their analysis. In this paper, deep learning techniques are investigated to extract decision dependencies and conditional clauses directly from text. By using a large dataset of labeled and tagged sentences and NLP, deep learning techniques (BERT and BI-LSTM-CRF) are trained and tested on the identification of these items. The results show that the performance is sufficiently high to extract decision dependency and logic (semi)-automatically from text which provides a big step towards automatic decision modelling.

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Notes

  1. 1.

    https://github.com/huggingface/neuralcoref.

  2. 2.

    https://pypi.org/project/NERDA/.

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Authors and Affiliations

  1. Leuven Institute for Research on Information Systems (LIRIS), KU Leuven, Leuven, Belgium

    Alexandre Goossens, Michelle Claessens, Charlotte Parthoens & Jan Vanthienen

Authors
  1. Alexandre Goossens
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  2. Michelle Claessens
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  3. Charlotte Parthoens
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  4. Jan Vanthienen
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Corresponding author

Correspondence to Alexandre Goossens .

Editor information

Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Andrea Marrella

  2. University of St. Gallen, St. Gallen, Switzerland

    Barbara Weber

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Goossens, A., Claessens, M., Parthoens, C., Vanthienen, J. (2022). Extracting Decision Dependencies and Decision Logic from Text Using Deep Learning Techniques. In: Marrella, A., Weber, B. (eds) Business Process Management Workshops. BPM 2021. Lecture Notes in Business Information Processing, vol 436. Springer, Cham. https://doi.org/10.1007/978-3-030-94343-1_27

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  • DOI: https://doi.org/10.1007/978-3-030-94343-1_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94342-4

  • Online ISBN: 978-3-030-94343-1

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