Combining Natural Language Processing and Blockchain for Smart Contract Generation in the Accounting and Legal Field | SpringerLink
Skip to main content

Combining Natural Language Processing and Blockchain for Smart Contract Generation in the Accounting and Legal Field

  • Conference paper
  • First Online:
Intelligent Human Computer Interaction (IHCI 2020)

Abstract

The growth of legislation and the demand for systems for automation of tasks has increased the demand for software development, so that we have greater assertiveness and speed in the reading and interpretation of laws in legal activities. Currently, written legislation must be interpreted by an analyst to be encoded in computer programs later, which is often error-prone. In this context, with the popularization of cryptocurrencies, interest was aroused for the use of Blockchain in the legal area. In particular, the use of smart contracts can house business rules in legislation and automate block control. Still, fast, quality code writing can benefit from the use of natural language processing (NLP) to help developers. After revisiting the state-of-the-art, it is perceived that there are no works that unite intelligent contracts and natural language processing in the context of the analysis of legislation. This work presents a computational model to generate intelligent codes from the analysis of legislation, using NLP and Blockchain for such a procedure. The practical and scientific contribution is pertinent for the law to be interpreted in the correct way and in accordance with the latest updates. Also, a prototype and initial tests are presented, which are encouraging and show the relevance of the research theme.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • 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

Similar content being viewed by others

Notes

  1. 1.

    Scalability, interoperability, speed to process transactions and regulation.

  2. 2.

    grep, awk, and sed.

References

  1. Sultan, K., Ruhi, U., Lakhani, R.: Conceptualizing blockchains: characteristics & applications (2018)

    Google Scholar 

  2. Maesa, D., Mori, P.: Blockchain 3.0 applications survey. J. Parallel Distrib. Comput. 138, 99–114 (2020). https://doi.org/10.1016/j.jpdc.2019.12.019

    Article  Google Scholar 

  3. Salah, K., Rehman, H.U., Nizamuddin, M., Al-Fuqaha, A.: Blockchain for AI: review and open research challenges. IEEE Access 7, 10127–10149 (2018)

    Article  Google Scholar 

  4. Butijn, B.-J., Tamburri, D., Heuvel, W.-J.: Blockchains: a systematic multivocal literature review. ACM Comput. Surv. 53, 1–37 (2020). https://doi.org/10.1145/3369052

    Article  Google Scholar 

  5. Porru, S., Pinna, A. Marchesi, M., Tonelli, R.: Blockchain-oriented software engineering: challenges and new directions. In: 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C), Buenos Aires, pp. 169–171 (2017). https://doi.org/10.1109/ICSE-C.2017.142

  6. do Amaral, G.L., et al.: Quantidade de normas editadas no Brasil: 28 anos da constituição federal de 1998. IBPT (2018). https://www.conjur.com.br/dl/estudo-ibpt-edicao-criacao-leis.pdf. Accessed 20 Mar 2020

  7. Marques, C.: Ambiguidade no Direito: Algumas Considerações. Revista Diálogos. 74–82 (2011). https://doi.org/10.13115/2236-1499.2011v1n4p74

  8. Gill, S.S., et al.: Transformative effects of IoT, blockchain and artificial intelligence on cloud computing: evolution, vision, trends and open challenges. Internet Things 8, 100118 (2019). https://doi.org/10.1016/j.iot.2019.100118

    Article  Google Scholar 

  9. Almasoud, A.S., Eljazzar, M.M., Hussain, F.: Toward a self-learned smart contracts. In: 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE), Xi’an, pp. 269–273 (2018). https://doi.org/10.1109/ICEBE.2018.00051

  10. Khurana, D., Koli, A., Khatter, K., Singh, S.: Natural language processing: state of the art, current trends and challenges (2017). arxiv.org/abs/1708.05148

  11. Nadkarni, P., Ohno-Machado, L., Chapman, W.: Natural language processing: an introduction. J. Am. Med. Inf. Assoc.: JAMIA. 18, 544–51 (2011). https://doi.org/10.1136/amiajnl-2011-000464

    Article  Google Scholar 

  12. Meziane, F., Athanasakis, N., Ananiadou, S.: Generating natural language specifications from UML class diagrams. Requir. Eng. 13, 1–18 (2008). https://doi.org/10.1007/s00766-007-0054-0

    Article  Google Scholar 

  13. Sureka, A., Mirajkar, P., Indukuri, K.: A rapid application development framework for rule-based named-entity extraction, p. 25 (2009). https://doi.org/10.1145/1517303.1517330

  14. Deeptimahanti, D., Sanyal, R.: Semi-automatic generation of UML models from natural language requirements. In: Proceedings of the 4th India Software Engineering Conference 2011, ISEC 2011, pp. 165–174 (2011). https://doi.org/10.1145/1953355.1953378

  15. Olajubu, O.: A textual domain specific language for requirement modelling. In: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2015), pp. 1060–1062. Association for Computing Machinery, New York (2015). 2786805.2807562

    Google Scholar 

  16. Billings, J., McCaskey, A., Vallee, G., Watson, G.:Will humans even write code in 2040 and what would that mean for extreme heterogeneity in computing? (2017). arXiv:1712.00676

  17. Lee, B.-S., Bryant, B.: Automated conversion from requirements documentation to an object-oriented formal specification language, p. 932 (2002). https://doi.org/10.1145/508969.508972

  18. Jaramillo, C.M.Z.: Computational linguistics for helping requirements elicitation: a dream about automated software development. In: Proceedings of the NAACL HLT 2010 Young Investigators Workshop on Computational Approaches to Languages of the Americas (YIWCALA 2010), pp. 117–124. Association for Computational Linguistics, USA (2010)

    Google Scholar 

  19. Martinez, A.R.: Natural language processing. Wiley Interdisc. Rev.: Comput. Stat. 2, 352–357 (2010). https://doi.org/10.1002/wics.76

    Article  Google Scholar 

  20. Sawai, S., et al.: Knowledge representation and machine translation. In: Proceedings of the 9th Conference on Computational Linguistics (COLING 1982), vol. 1, pp. 351–356. Academia Praha, CZE (1982). https://doi.org/10.3115/991813.991870

  21. NILCS Corpora: Núcleo Interinstitucional de Linguistica Computacional (2000). http://www.nilc.icmc.usp.br/nilc/tools/corpora.htm. Accessed 06 Mar 2020

  22. Quirk, C., et al.: Language to code: learning semantic parsers for if-this-then-that recipes. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL-15), pp. 878–888, Beijing, China, July 2015

    Google Scholar 

  23. Osman, M.S., et al.: Generate use case from the requirements written in a natural language using machine learning. In: IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Amman, Jordan, pp. 748–751 (2019). https://doi.org/10.1109/JEEIT.2019.8717428

  24. Hamza, Z.A., Hammad, M.: Generating UML use case models from software requirements using natural language processing. In: 2019 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO), Manama, Bahrain, pp. 1–6 (2019). https://doi.org/10.1109/ICMSAO.2019.8880431

  25. More, P.R., Phalnikar, R.: Generating UML diagrams from natural language specifications. Int. J. Appl. Inf. Syst. 1, 19–23 (2012)

    Google Scholar 

  26. Angstadt, K., Weimer, W., Skadron, K.: RAPID programming of pattern-recognition processors. SIGPLAN Not. 51(4), 593–605 (2016). https://doi.org/10.1145/2954679.2872393

    Article  Google Scholar 

  27. Angstadt, K., Weimer, W., Skadron, K.: RAPID programming of pattern-recognition processors. SIGARCH Comput. Archit. News 44(2), 593–605 (2016). https://doi.org/10.1145/2980024.2872393

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dhananjay Singh .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Monteiro, E., Righi, R., Kunst, R., da Costa, C., Singh, D. (2021). Combining Natural Language Processing and Blockchain for Smart Contract Generation in the Accounting and Legal Field. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12615. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-68449-5_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68448-8

  • Online ISBN: 978-3-030-68449-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics