Abstract
This study addresses the pressing issue of farmer suicides in India, a problem that has escalated significantly since 1995, with over 0.3 million recorded cases. While scholars and activists have proposed various factors contributing to these suicides, quantitative insights into the influence and probabilistic significance of these factors remain elusive. Consequently, we introduce a pioneering two-tier knowledge engineering framework to tackle this challenge. In the first tier, we employ natural language processing to gather a comprehensive array of suicide-causing factors from news articles and blog posts on the World Wide Web. In the second tier, we undertake a meticulous analysis of the causal factors by calculating probabilities and categorising the identified causal factors into distinct groups: social, economic, socio-economic, nature-related, health-related, and governmental policies. Our analysis reveals that health-related causal factors account for over 50% of farmer suicides, while economic, social, and socio-economic factors contribute to 26%. By meticulously investigating these causal elements, our approach has the potential to substantially mitigate future instances of farmer suicides.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3((Feb)), 1137–1155 (2003)
Bokinala, V.R.: Investigation into the causes and potential mitigation of the high incidence of farmers suicide (2022)
Calicioglu, O., Flammini, A., Bracco, S., Bellù, L., Sims, R.: The future challenges of food and agriculture: an integrated analysis of trends and solutions. Sustainability 11(1), 222 (2019)
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 (2003)
Girju, R., Moldovan, D.I., et al.: Text mining for causal relations. In: FLAIRS Conference, pp. 360–364 (2002)
Grech, M.R., Horberry, T., Smith, A.: Human error in maritime operations: analyses of accident reports using the leximancer tool. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 46, pp. 1718–1721. Sage Publications Sage CA: Los Angeles, CA (2002)
Hänninen, M., Sladojevic, M., Tirunagari, S., Kujala, P.: Feasibility of collision and grounding data for probabilistic accident modelling. Collision and grounding of ships and offshore structures, pp. 1–8. CRC Press/Taylor and Francis Group, London (2013)
Sahoo, J.: Modified TF-IDF term weighting strategies for text categorization (2018). https://doi.org/10.1109/INDICON.2017.8487593
Tirunagari, S.: Data mining of causal relations from text: analysing maritime accident investigation reports. arXiv preprint arXiv:1507.02447 (2015)
Tirunagari, S., Bokinala, V.: Dataset: investigation into the causes and potential mitigation of the high incidence of farmers’ suicide. figshare (2023). https://doi.org/10.6084/m9.figshare.24316948
Tirunagari, S., Hanninen, M., Stanhlberg, K., Kujala, P.: Mining causal relations and concepts in maritime accidents investigation reports. Int. J. Innovative Res. Dev. 1(10), 548–566 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bokinala, V., Tirunagari, S., Mohan, S. (2024). A Knowledge Engineering Framework Addressing High Incidence of Farmer Suicides. In: Chakravarthi, B.R., et al. Speech and Language Technologies for Low-Resource Languages. SPELLL 2023. Communications in Computer and Information Science, vol 2046. Springer, Cham. https://doi.org/10.1007/978-3-031-58495-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-58495-4_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-58494-7
Online ISBN: 978-3-031-58495-4
eBook Packages: Computer ScienceComputer Science (R0)