Skip to main content
SpringerLink
Log in

An Overview of Ways of Discovering Cause-Effect Relations in Text by Using Natural Language Processing

  • Conference paper
  • First Online:
Evaluation of Novel Approaches to Software Engineering (ENASE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1172))

  • 889 Accesses

Abstract

Understanding of cause-effect relations is vital for constructing a valid model of a system under development. Discovering cause-effect relations in text is one of the difficult tasks in Natural Language Processing (NLP). This paper presents a survey on trends in this field related to understanding how linguistically causal dependencies can be expressed in the text, what patterns and models exist, which of them are more and less successful and why. The results show that causal dependencies in text can be described using plenty lexical expressions as well as linguistic and syntactic patterns. Moreover, the same constructs can be used for non-causal dependencies. Solutions that combine the patterns, ontologies, temporal models and a use of machine learning demonstrate more accurate results in extracting and selecting cause-effect pairs. However, not all lexical expressions are well studied. There are few researches on multi-cause and multi-effect domains. The results of the survey are to be used for construction of a Topological Functioning Model (TFM) of a system, where cause-effect relations are one of key elements. However, they can be used also for construction of other behavioral models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Miller, J., Mukerji, J.: Model driven architecture (MDA) (2001)

    Google Scholar 

  2. Khoo, C., Chan, S., Niu, Y.: The many facets of the cause-effect relation. In: Green, R., Bean, C.A., Myaeng, S.H. (eds.) The Semantics of Relationships. ISKM, vol. 3, pp. 51–70. Springer, Dordrecht (2002). https://doi.org/10.1007/978-94-017-0073-3_4

    Chapter  Google Scholar 

  3. Nazaruka, E.: Meaning of cause-and-effect relations of the topological functioning model in the UML analysis model. In: Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering, pp. 336–345. SciTePress - Science and Technology Publications (2017)

    Google Scholar 

  4. Kardoš, M., Drozdová, M.: Analytical method of CIM to PIM transformation in model driven architecture (MDA). J. Inf. Organ. Sci. 34, 89–99 (2010)

    Google Scholar 

  5. Kriouile, A., Gadi, T., Balouki, Y.: CIM to PIM transformation: a criteria based evaluation. Int. J. Comput. Technol. Appl. 4, 616–625 (2013)

    Google Scholar 

  6. Kriouile, A., Addamssiri, N., Gadi, T., Balouki, Y.: Getting the static model of PIM from the CIM. In: 2014 Third IEEE International Colloquium in Information Science and Technology (CIST), pp. 168–173. IEEE, Tetouan (2014)

    Google Scholar 

  7. Kriouile, A., Addamssiri, N., Gadi, T.: An MDA method for automatic transformation of models from CIM to PIM. Am. J. Softw. Eng. Appl. 4, 1–14 (2015). https://doi.org/10.11648/j.ajsea.20150401.11

    Article  Google Scholar 

  8. Bousetta, B., El Beggar, O., Gadi, T.: A methodology for CIM modelling and its transformation to PIM. J. Inf. Eng. Appl. 3, 1–21 (2013)

    Google Scholar 

  9. Rhazali, Y., Hadi, Y., Mouloudi, A.: CIM to PIM transformation in MDA: from service-oriented business models to web-based design models. Int. J. Softw. Eng. Appl. 10, 125–142 (2016). https://doi.org/10.14257/ijseia.2016.10.4.13

    Article  Google Scholar 

  10. Essebaa, I., Chantit, S.: Toward an automatic approach to get PIM level from CIM level using QVT rules. In: 2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA), pp. 1–6. IEEE, Mohammedia (2016)

    Google Scholar 

  11. Osis, J., Donins, U.: Topological UML Modeling: An Improved Approach for Domain Modeling and Software Development. Elsevier, Amsterdam (2017)

    Book  Google Scholar 

  12. Osis, J.: Topological model of system functioning. Autom. Comput. Sci. J. Acad. Sci. 6, 44–50 (1969). (in Russian)

    MathSciNet  Google Scholar 

  13. Osis, J., Asnina, E., Grave, A.: Formal computation independent model of the problem domain within the MDA. In: Zendulka, J. (ed.) Proceedings of the 10th International Conference on Information System Implementation and Modeling, Hradec nad Moravicí, Czech Republic, 23–25 April 2007, pp. 47–54. Jan Stefan MARQ (2007)

    Google Scholar 

  14. Osis, J., Asnina, E.: Topological modeling for model-driven domain analysis and software development : functions and architectures. In: Model-Driven Domain Analysis and Software Development: Architectures and Functions, pp. 15–39. IGI Global, Hershey (2011)

    Google Scholar 

  15. Osis, J., Slihte, A.: Transforming textual use cases to a computation independent model. In: Osis, J., Nikiforova, O. (eds.) Model-Driven Architecture and Modeling-Driven Software Development: ENASE 2010, 2nd MDA&MTDD Whs., pp. 33–42. SciTePress (2010)

    Google Scholar 

  16. Slihte, A., Osis, J., Donins, U.: Knowledge integration for domain modeling. In: Osis, J., Nikiforova, O. (eds.) Model-Driven Architecture and Modeling-Driven Software Development: ENASE 2011, 3rd MDA&MDSD Whs., pp. 46–56. SciTePress (2011)

    Google Scholar 

  17. Elstermann, M., Heuser, T.: Automatic tool support possibilities for the text-based S-BPM process modelling methodology. In: Proceedings of the 8th International Conference on Subject-Oriented Business Process Management, S-BPM 2016, pp. 1–8. ACM Press, New York (2016)

    Google Scholar 

  18. Nazaruka, E.: Identification of causal dependencies by using natural language processing: a survey. In: Damian, E., Spanoudakis, G., Maciaszek, L. (eds.) Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: MDI4SE, pp. 603–613. SciTePress (2019)

    Google Scholar 

  19. Osis, J., Asnina, E.: Is modeling a treatment for the weakness of software engineering? In: Model-Driven Domain Analysis and Software Development, pp. 1–14. IGI Global, Hershey (2011)

    Google Scholar 

  20. Asnina, E.: The computation independent viewpoint: a formal method of topological functioning model constructing. Appl. Comput. Syst. 26, 21–32 (2006)

    Google Scholar 

  21. Osis, J., Asnina, E., Grave, A.: MDA oriented computation independent modeling of the problem domain. In: Proceedings of the 2nd International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2007, pp. 66–71. INSTICC Press, Barcelona (2007)

    Google Scholar 

  22. Osis, J., Asnina, E., Grave, A.: Formal problem domain modeling within MDA. In: Filipe, J., Shishkov, B., Helfert, M., Maciaszek, L.A. (eds.) ENASE/ICSOFT -2007. CCIS, vol. 22, pp. 387–398. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88655-6_29

    Chapter  Google Scholar 

  23. Šlihte, A., Osis, J.: The integrated domain modeling: a case study. In: Proceedings of the 11th International Baltic Conference on Databases and Information Systems (DB&IS 2014), pp, 465–470. Tallinn University of Technology Press, Tallinn (2014)

    Google Scholar 

  24. Asnina, E., Ovchinnikova, V.: Specification of decision-making and control flow branching in topological functioning models of systems. In: International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2015), pp. 364–373. SciTePress, Barcelona (2015)

    Google Scholar 

  25. Khoo, C., Chan, S., Niu, Y., Ang, A.: A method for extracting causal knowledge from textual databases. Singap. J. Libr. & Inf. Manag. 28, 48–63 (1999)

    Google Scholar 

  26. Solstad, T., Bott, O.: Causality and causal reasoning in natural language. In: Waldmann, M.R. (ed.) The Oxford Handbook of Causal Reasoning. Oxford University Press, Oxford (2017)

    Google Scholar 

  27. Ning, Q., Feng, Z., Wu, H., Roth, D.: Joint reasoning for temporal and causal relations. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers), pp. 2278–2288. Association for Computational Linguistics, Melbourne (2018)

    Google Scholar 

  28. Altenberg, B.: Causal linking in spoken and written English. Stud. Linguist. 38, 20–69 (1984)

    Article  Google Scholar 

  29. Girju, R.: Automatic detection of causal relations for question answering. In: Proceedings of the ACL 2003 Workshop on Multilingual Summarization and Question Answering, pp. 76–83. Association for Computational Linguistics, Morristown (2003)

    Google Scholar 

  30. Corrigan, R.: Causal attributions to the states and events encoded by different types of verbs. Br. J. Soc. Psychol. 32, 335–348 (1993)

    Article  Google Scholar 

  31. Corrigan, R., Stevenson, C.: Children’s causal attribution to states and events described by different classes of verbs. Cogn. Dev. 9, 235–256 (1994)

    Article  Google Scholar 

  32. Waldmann, M.R., Hagmayer, Y.: Causal reasoning. In: Reisberg, D. (ed.) Oxford Handbook of Cognitive Psychology. Oxford University Press, New York (2013)

    Google Scholar 

  33. Asghar, N.: Automatic extraction of causal relations from natural language texts : a comprehensive survey. CoRR abs/1605.0 (2016)

    Google Scholar 

  34. Mirza, P.: Extracting temporal and causal relations between events. In: Proceedings of the ACL 2014 Student Research Workshop, pp. 10–17. Association for Computational Linguistics, Baltimore (2014)

    Google Scholar 

  35. Mostafazadeh, N., Grealish, A., Chambers, N., Allen, J., Vanderwende, L.: CaTeRS : causal and temporal relation scheme for semantic annotation of event structures. In: Proceedings of the Fourth Workshop on Events, pp. 51–61. Association for Computational Linguistics, San Diego (2016)

    Google Scholar 

  36. Khoo, C.S.G., Kornfilt, J., Oddy, R.N., Myaeng, S.H.: Automatic extraction of cause-effect information from newspaper text without knowledge-based inferencing. Lit. Linguist. Comput. 13, 177–186 (1998)

    Article  Google Scholar 

  37. Hendrickx, I., et al.: SemEval-2010 task 8: multi-way classification of semantic relations between pairs of nominals. In: Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, Uppsala, Sweden, 15–16 July 2010, pp. 33–38. Association for Computational Linguistics (2010)

    Google Scholar 

  38. Sorgente, A., Vettigli, G., Mele, F.: Automatic extraction of cause-effect relations in natural language text. In: Lai, C., Semeraro, G., Giuliani, A. (eds.) Proceedings of the 7th International Workshop on Information Filtering and Retrieval Co-Located with the 13th Conference of the Italian Association for Artificial Intelligence (AI*IA 2013), pp. 37–48 (2013)

    Google Scholar 

  39. Dasgupta, T., Saha, R., Dey, L., Naskar, A.: Automatic extraction of causal relations from text using linguistically informed deep neural networks. In: Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pp. 306–316. Association for Computational Linguistics, Stroudsburg (2018)

    Google Scholar 

  40. Mausam, Schmitz, M., Bart, R., Soderland, S., Etzioni, O.: Open language learning for information extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 523–534. Association for Computational Linguistics (2012)

    Google Scholar 

  41. Kang, D., Gangal, V., Lu, A., Chen, Z., Hovy, E.: Detecting and explaining causes from text for a time series event. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2758–2768. The Association for Computational Linguistics (2017)

    Google Scholar 

  42. Pearl, J.: The seven tools of causal inference, with reflections on machine learning. Commun. Assoc. Comput. Mach. 62, 54–60 (2019). https://doi.org/10.1145/3241036

    Article  Google Scholar 

  43. Blanco, E., Castell, N., Moldovan, D.: Causal relation extraction. In: Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC 2008), pp. 28–30. European Language Resources Association (ELRA) (2008)

    Google Scholar 

  44. Blass, J.A., Forbus, K.D.: Natural language instruction for analogical reasoning : an initial report. In: Workshops Proceedings for the Twenty-Fourth International Conference on Case-Based Reasoning (ICCBR 2016), pp. 21–30 (2016)

    Google Scholar 

  45. Cao, M., Sun, X., Zhuge, H.: The contribution of cause-effect link to representing the core of scientific paper—the role of Semantic Link Network. PLoS ONE 13, 1–14 (2018). https://doi.org/10.1371/journal.pone.0199303

    Article  Google Scholar 

  46. Sorgente, A., Vettigli, G., Mele, F.: A hybrid approach for the automatic extraction of causal relations from text. In: Lai, C., Giuliani, A., Semeraro, G. (eds.) Emerging Ideas on Information Filtering and Retrieval. SCI, vol. 746, pp. 15–29. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-68392-8_2

    Chapter  Google Scholar 

  47. Mueller, R., Hüttemann, S.: Extracting causal claims from information systems papers with natural language processing for theory ontology learning. In: Proceedings of the 51st Hawaii International Conference on System Sciences (HICSS). IEEE Computer Society Press, Hawaii (2018)

    Google Scholar 

  48. Schneider, G., Winters, J.P.: Applying Use Cases: A practical Guide, 2nd edn. Pearson Education Inc., London (2001)

    Google Scholar 

  49. Leffingwell, D., Widrig, D.: Managing Software Requirements: A Use Case Approach, 2nd edn. Addison-Wesley, Boston (2003)

    Google Scholar 

  50. Elallaoui, M., Nafil, K., Touahni, R.: Automatic transformation of user stories into UML use case diagrams using NLP techniques. Procedia Comput. Sci. 130, 42–49 (2018). https://doi.org/10.1016/j.procs.2018.04.010

    Article  Google Scholar 

  51. Azzazi, A.: A framework using NLP to automatically convert user-stories into use cases in software projects. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 17, 71–76 (2017)

    Google Scholar 

  52. Lucassen, G., Robeer, M., Dalpiaz, F., van der Werf, J.M.E.M., Brinkkemper, S.: Extracting conceptual models from user stories with Visual Narrator. Requir. Eng. 22, 339–358 (2017). https://doi.org/10.1007/s00766-017-0270-1

    Article  Google Scholar 

  53. Masud, M., Iqbal, M., Khan, M.U., Azam, F.: Automated user story driven approach for web-based functional testing. Int. J. Comput. Inf. Eng. 11, 91–98 (2017)

    Google Scholar 

  54. Robeer, M., Lucassen, G., van der Werf, J.M.E.M., Dalpiaz, F., Brinkkemper, S.: Automated extraction of conceptual models from user stories via NLP. In: 2016 IEEE 24th International Requirements Engineering Conference (RE), pp. 196–205. IEEE (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Riga Technical University, 1 Setas Str., Riga, 1048, Latvia

    Erika Nazaruka

Authors
  1. Erika Nazaruka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erika Nazaruka .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates

    Ernesto Damiani

  2. Department of Computer Science, City University of London, London, UK

    George Spanoudakis

  3. Wroclaw University of Economics, Wroclaw, Poland

    Leszek A. Maciaszek

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nazaruka, E. (2020). An Overview of Ways of Discovering Cause-Effect Relations in Text by Using Natural Language Processing. In: Damiani, E., Spanoudakis, G., Maciaszek, L. (eds) Evaluation of Novel Approaches to Software Engineering. ENASE 2019. Communications in Computer and Information Science, vol 1172. Springer, Cham. https://doi.org/10.1007/978-3-030-40223-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-40223-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-40222-8

  • Online ISBN: 978-3-030-40223-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation