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Deep Learning for the Identification of Decision Modelling Components from Text

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Rules and Reasoning (RuleML+RR 2021)

Abstract

Decision and process descriptions often find themselves encapsulated in long descriptions such as regulations or guidelines. Decision modelling aims at modelling the structure and logic of a decision. For decision modellers, analysing textual documents in search for relevant sentences is a time consuming activity. A promising research topic is to build decision models from text. In this paper, an automatic decision modelling component classifier using deep learning is proposed. Using a large dataset consisting of labeled sentences, the usability of deep learning techniques is investigated. In total three deep learning techniques are evaluated and compared to non-deep learning techniques using both Bag of Words and Term Frequency-Inverse Document Frequency. We conclude that classifying decision modelling components is possible and report that BERT for sequence classification is the best performing technique.

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Notes

  1. 1.

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

  2. 2.

    https://github.com/NielsRogge/Description2Process.

References

  1. van der Aa, H., Di Ciccio, C., Leopold, H., Reijers, H.A.: Extracting declarative process models from natural language. In: Giorgini, P., Weber, B. (eds.) CAiSE 2019. LNCS, vol. 11483, pp. 365–382. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21290-2_23

    Chapter  Google Scholar 

  2. van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)

    Article  Google Scholar 

  3. de AR Goncalves, J.C., Santoro, F.M., Baiao, F.A.: Business process mining from group stories. In: 2009 13th International Conference on Computer Supported Cooperative Work in Design, pp. 161–166. IEEE (2009)

    Google Scholar 

  4. Arco, L., Nápoles, G., Vanhoenshoven, F., Lara, A.L., Casas, G., Vanhoof, K.: Natural language techniques supporting decision modelers. Data Min. Knowl. Disc. 35(1), 290–320 (2020). https://doi.org/10.1007/s10618-020-00718-4

    Article  MathSciNet  Google Scholar 

  5. Bazhenova, E., Buelow, S., Weske, M.: Discovering decision models from event logs. In: Abramowicz, W., Alt, R., Franczyk, B. (eds.) BIS 2016. LNBIP, vol. 255, pp. 237–251. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39426-8_19

    Chapter  Google Scholar 

  6. Bazhenova, E., Weske, M.: Deriving decision models from process models by enhanced decision mining. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 444–457. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42887-1_36

    Chapter  Google Scholar 

  7. Danenas, P., Skersys, T., Butleris, R.: Natural language processing-enhanced extraction of SBVR business vocabularies and business rules from UML use case diagrams. Data Knowl. Eng. 128, 101822 (2020)

    Google Scholar 

  8. De Smedt, J., Hasić, F., vanden Broucke, S.K., Vanthienen, J.: Holistic discovery of decision models from process execution data. Knowl.-Based Syst. 183, 104866 (2019)

    Google Scholar 

  9. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  10. Dragoni, M., Villata, S., Rizzi, W., Governatori, G.: Combining NLP approaches for rule extraction from legal documents. In: 1st Workshop on MIning and REasoning with Legal texts (MIREL 2016) (2016)

    Google Scholar 

  11. Epure, E.V., Martín-Rodilla, P., Hug, C., Deneckère, R., Salinesi, C.: Automatic process model discovery from textual methodologies. In: 2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS), pp. 19–30. IEEE (2015)

    Google Scholar 

  12. Etikala, V., Van Veldhoven, Z., Vanthienen, J.: Text2Dec: extracting decision dependencies from natural language text for automated DMN decision modelling. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds.) BPM 2020. LNBIP, vol. 397, pp. 367–379. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66498-5_27

    Chapter  Google Scholar 

  13. Friedrich, F., Mendling, J., Puhlmann, F.: Process model generation from natural language text. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 482–496. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21640-4_36

    Chapter  Google Scholar 

  14. Ghose, A., Koliadis, G., Chueng, A.: Process discovery from model and text artefacts. In: 2007 IEEE Congress on Services (Services 2007), pp. 167–174. IEEE (2007)

    Google Scholar 

  15. Goossens, A., Claessens, M., Parthoens, C., Vanthienen, J.: Extracting decision dependencies and decision logic using deep learning techniques, BPM 2021 DEC2H Workshop (2021)

    Google Scholar 

  16. Hadeer, A., Issa, T., Sherif, S.: Detecting opinion spams and fake news using text classification. Secur. Priv. 1(1), e9 (2018)

    Google Scholar 

  17. Honkisz, K., Kluza, K., Wiśniewski, P.: A concept for generating business process models from natural language description. In: Liu, W., Giunchiglia, F., Yang, B. (eds.) KSEM 2018. LNCS (LNAI), vol. 11061, pp. 91–103. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99365-2_8

    Chapter  Google Scholar 

  18. Luss, R., d’Aspremont, A.: Predicting abnormal returns from news using text classification. Quantit. Finan. 15(6), 999–1012 (2015)

    Article  MathSciNet  Google Scholar 

  19. Martin, L., et al.: CamemBERT: a tasty French language model. arXiv preprint arXiv:1911.03894 (2019)

  20. Melville, P., Gryc, W., Lawrence, R.D.: Sentiment analysis of blogs by combining lexical knowledge with text classification. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1275–1284 (2009)

    Google Scholar 

  21. Mirończuk, M.M., Protasiewicz, J.: A recent overview of the state-of-the-art elements of text classification. Expert Syst. Appl. 106, 36–54 (2018)

    Article  Google Scholar 

  22. OMG: Business process model and notation 1.0 (2010). https://www.omg.org/spec/BPMN/1.0

  23. OMG: Decision model and notation 1.0 (2015). https://www.omg.org/spec/DMN/1.0/

  24. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  25. Polignano, M., Basile, P., de Gemmis, M., Semeraro, G., Basile, V.: AlBERTo: Italian BERT language understanding model for NLP challenging tasks based on Tweets. In: Proceedings of the Sixth Italian Conference on Computational Linguistics (CLiC-it 2019), vol. 2481. CEUR (2019). https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074851349&partnerID=40&md5=7abed946e06f76b3825ae5e294ffac14

  26. Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should I trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)

    Google Scholar 

  27. Sànchez-Ferreres, J., Burattin, A., Carmona, J., Montali, M., Padró, L.: Formal reasoning on natural language descriptions of processes. In: Hildebrandt, T., van Dongen, B.F., Röglinger, M., Mendling, J. (eds.) BPM 2019. LNCS, vol. 11675, pp. 86–101. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26619-6_8

    Chapter  Google Scholar 

  28. Sanchez-Pi, N., Martí, L., Garcia, A.C.B., et al.: Text classification techniques in oil industry applications. In: Herrero, Á. (ed.) International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. AISC, pp. 211–220. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-319-01854-6_22

    Chapter  Google Scholar 

  29. Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 (2019)

  30. Scheible, R., Thomczyk, F., Tippmann, P., Jaravine, V., Boeker, M.: GottBERT: a pure German language model. arXiv preprint arXiv:2012.02110 (2020)

  31. Sinha, A., Paradkar, A.: Use cases to process specifications in business process modeling notation. In: 2010 IEEE International Conference on Web Services, pp. 473–480. IEEE (2010)

    Google Scholar 

  32. Thangaraj, M., Sivakami, M.: Text classification techniques: a literature review. Interdisc. J. Inf. Knowl. Manag. 13 (2018)

    Google Scholar 

  33. Vanthienen, J.: Decisions, advice and explanation: an overview and research agenda. A Research Agenda for Knowledge Management and Analytics (2021)

    Google Scholar 

  34. Wang, H.J., Zhao, J.L., Zhang, L.J.: Policy-driven process mapping (PDPM): discovering process models from business policies. Decis. Support Syst. 48(1), 267–281 (2009)

    Article  Google Scholar 

  35. Wolf, T., et al.: HuggingFace’s transformers: state-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019)

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Goossens, A., Claessens, M., Parthoens, C., Vanthienen, J. (2021). Deep Learning for the Identification of Decision Modelling Components from Text. In: Moschoyiannis, S., Peñaloza, R., Vanthienen, J., Soylu, A., Roman, D. (eds) Rules and Reasoning. RuleML+RR 2021. Lecture Notes in Computer Science(), vol 12851. Springer, Cham. https://doi.org/10.1007/978-3-030-91167-6_11

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  • DOI: https://doi.org/10.1007/978-3-030-91167-6_11

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