Advertisement

Implementation of Smart Legal Assistance System in Accordance with the Indian Penal Code Using Similarity Measures

  • Dipti Pawade
  • Avani Sakhapara
  • Hussain Ratlamwala
  • Siddharth MishraEmail author
  • Samreen Shaikh
  • Dhrumil Mehta
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1046)

Abstract

The rate of crime is increasing rapidly and many citizens of country are the victims of these crimes. It is observed that though the number of crimes taking place is very huge, the actual number of crimes being reported to the legal authorities is very small. This huge difference is because of several reasons where one of the reason is lack of awareness about the civil rights and the laws. Many citizens in India are unaware about the evil practices performed against them and unethical exploitation of them done by others. The citizens are also not aware about various laws in the Indian Penal Code. So inorder to bridge this gap between the laws and the common citizens of India, we have proposed a Natural Language Processing (NLP) system using Word Mover’s Distance (WMD) which takes as input the textual description of crime and generates as output, the applicable Indian Penal Code (IPC) sections and the description of the punishments mentioned under these sections. The performance comparison of WMD with cosine similarity is presented. Finally, the accuracy of the system is measured by getting the result generated by the system to be validated by a lawyer personal.

Keywords

Indian Penal Code Natural language programming Word Mover’s Distance Cosine Rapid Automated Keyword Extraction 

References

  1. 1.
    National Crime Records Bureau data, 2015: slight dip in rape, crime against women. The Indian Express (2019). https://indianexpress.com/article/explained/national-crime-records-bureau-data-2015-slight-dip-in-rape-crime-against-women-3004980/
  2. 2.
    Lende, S.P., Raghuwanshi, M.M.: Closed domain question answering system using NLP techniques. Int. J. Eng. Sci. Res. Technol. (IJESRT) 5(4), 632–639 (2016).  https://doi.org/10.5281/zenodo.49808CrossRefGoogle Scholar
  3. 3.
    Pham, S.T., Nguyen, D.T.: Implementation method of answering engine for vietnamese questions in reading answering system model (RASM). In: 8th Asia Modelling Symposium (AMS), Taipei, Taiwan, pp. 175–180 (2014).  https://doi.org/10.1109/AMS.2014.42
  4. 4.
    Wang, J., Man, C., Zhao, Y., Wang, F.: An answer recommendation algorithm for medical community question answering systems. In: IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), Beijing, China, pp. 139–144 (2016).  https://doi.org/10.1109/SOLI.2016.7551676
  5. 5.
    Devi, M., Dua, M.: ADANS: an agriculture domain question answering system using ontologies. In: IEEE Proceedings of International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India, pp. 122–127 (2017).  https://doi.org/10.1109/CCAA.2017.8229784
  6. 6.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  7. 7.
    Kamdi, R.P., Agrawal, A.J.: Keywords based closed domain question answering system for indian penal code sections and indian amendment laws. Int. J. Intell. Syst. Appl. (IJISA) 7(12), 57–67 (2015).  https://doi.org/10.5815/ijisa.2015.12.06CrossRefGoogle Scholar
  8. 8.
    Pudaruth, S., Gunputh, R.P., Soyjaudah, K.M.S., Domun, P.: A question answer system for the mauritian judiciary. In: IEEE Proceedings of 3rd International Conference on Soft Computing and Machine Intelligence (ISCMI), Dubai, United Arab Emirates, pp. 201–205 (2016).  https://doi.org/10.1109/ISCMI.2016.47
  9. 9.
    Quaresma, P., Rodrigues, I.P.: A question answer system for legal information retrieval. In: ACM Proceedings of Legal Knowledge and Information Systems: JURIX 2005: The Eighteenth Annual Conference, pp. 91–100 (2005)Google Scholar
  10. 10.
    Bick, E.: The Parsing System Palavras: Automatic Grammatical Analysis of Portuguese in a Constraint Grammar Framework. Aarhus University Press, Aarhus (2000)Google Scholar
  11. 11.
    Tirpude, S.C., Alvi, A.S.: Closed domain keyword based question answering system for legal documents of IPC sections and Indian Laws. Int. J. Innov. Res. Comput. Commun. Eng. (IJIRCCE) 3(6), 5299–5311 (2015).  https://doi.org/10.15680/ijircce.2015.0306077CrossRefGoogle Scholar
  12. 12.
    Kusner, M.J., Sun, Y., Kolkin, N.I., Weinberger, K.Q.: From word embeddings to document distances. In: ACM Proceedings of 32nd International Conference on Machine Learning (ICML), Lille, France, pp. 957–966 (2015)Google Scholar
  13. 13.
  14. 14.
    Sakhapara, A., Pawade, D., Chapanera, H., Jani, H., Ramgaonkar, D.: Segregation of similar and dissimilar live RSS news feeds based on similarity measures. In: Balas, V.E., Sharma, N., Chakrabarti, A. (eds.) Data Management, Analytics and Innovation. AISC, vol. 839, pp. 333–344. Springer, Singapore (2019).  https://doi.org/10.1007/978-981-13-1274-8_26CrossRefGoogle Scholar
  15. 15.
    Rose, S., Engel, D., Cramer, N., Cowley, W.: Automatic keyword extraction from individual documents. In: Berry, M.W., Kogan, J. (eds.) Text Mining: Applications and Theory, pp. 1–20. Wiley (2010).  https://doi.org/10.1002/9780470689646.ch1Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Dipti Pawade
    • 1
  • Avani Sakhapara
    • 1
  • Hussain Ratlamwala
    • 1
  • Siddharth Mishra
    • 1
    Email author
  • Samreen Shaikh
    • 1
  • Dhrumil Mehta
    • 1
  1. 1.Department of ITK.J. Somaiya College of EngineeringMumbaiIndia

Personalised recommendations