Chatbot as support to decision-making in the context of natural resource management
Resumo
The management of natural resources is becoming increasingly relevant due to its direct implication in society's life. Thus, individuals must make decisions based on environmental and social aspects. This work uses a chatbot to support users' decisions through an RPG scenario based on the participatory management of resources in the Lagoa Mirim Watershed and Canal São Gonçalo Basin. In this context, in addition to the chatbot, this study presents a pollution predictor to support decision-making, with a determination coefficient of 0.99, constructed using random forest. Also, we present five Word Embeddings models to expand the natural language understanding, based on a corpus of about 700 thousand sentences, capable of identifying relations between words.
Palavras-chave:
Chatbot, Machine learning, Natural Language Processing, Word Embeddings, Natural Resource Management
Referências
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Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135–146.
Drury, B., Fernandes, R., and Lopes, A. (2017). Bragrinews: Um corpus temporal-causal (portugues-brasileiro) para a agricultura. Linguamática, 9.
Eisenstein, J. (2019). Introduction to Natural Language Processing. Adaptive Computation and Machine Learning series. MIT Press, Cambridge, MA, USA.
Holzman, B. (2009). Natural resource management. [Online; accessed 18 fev. 2021] http://online.sfsu.edu/bholzman/courses/GEOG%20657/.
Hussain, S., Sianaki, O., and Ababneh, N. (2019). A Survey on Conversational Agents/Chatbots Classification and Design Techniques, pages 946–956. Springer International Publishing, Cham, DE.
Kannagi, L., Ramya, C., Shreya, R., and Sowmiya, R. (2018). Virtual conversational assistant:‘the farmbot’. International Journal of Engineering Technology Science and Research, 5(3):520–527.
Lane, H., Howard, C., and Hapke, H. (2019). Natural Language Processing in Action. Manning Publications, New York, NY, USA.
Leitzke, B., Farias, G., Melo, M., Gonçalves, M., Born, M., Rodrigues, P., Martins, V., Barbosa, R., Aguiar, M., and Adamatti, D. (2019). Sistema multiagente para gestão de recursos hídricos: Modelagem da bacia do são gonçalo e da lagoa mirim. In Workshop de Computação Aplicada a Gestão do Meio Ambiente e Recursos Naturais, pages 87– 96, Porto Alegre, RS, Brasil. SBC.
Mikolov, T., Chen, K., Corrado, G. S., and Dean, J. (2013). Efficient estimation of word representations in vector space.
Miñarro-Giménez, J. A., Marín-Alonso, O., and Samwald, M. (2015). Applying deep learning techniques on medical corpora from the world wide web: a prototypical system and evaluation.
Nallappan, M. (2018). A prediction system for farmers to enhance the agriculture yield using cognitive data science. International Journal of Advanced Research in Computer Science, 9:780–784.
Pennington, J., Socher, R., and Manning, C. (2014). GloVe: Global vectors for word representation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543.
Ponte, B., de la Fuente, D., ParreÑo, J., and Pino, R. (2016). Intelligent decision support system for real-time water demand management. International Journal of Computational Intelligence Systems, 9(1):168–183.
Raj, S. (2019). Building Chatbots with Python: Using Natural Language Processing and Machine Learning. Apress, New York, NY, USA.
Sawant, D., Jaiswal, A., Singh, J., and Shah, P. (2019). Agribot - an intelligent interactive interface to assist farmers in agricultural activities. In Proceedings of the IEEE Bombay Section Signature Conference (IBSSC), pages 1–6, Mumbai, India. IEEE.
Alpaydin, E. (2014). Introduction to Machine Learning. MIT Press, Cambridge, MA, USA, 3 edition.
Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135–146.
Drury, B., Fernandes, R., and Lopes, A. (2017). Bragrinews: Um corpus temporal-causal (portugues-brasileiro) para a agricultura. Linguamática, 9.
Eisenstein, J. (2019). Introduction to Natural Language Processing. Adaptive Computation and Machine Learning series. MIT Press, Cambridge, MA, USA.
Holzman, B. (2009). Natural resource management. [Online; accessed 18 fev. 2021] http://online.sfsu.edu/bholzman/courses/GEOG%20657/.
Hussain, S., Sianaki, O., and Ababneh, N. (2019). A Survey on Conversational Agents/Chatbots Classification and Design Techniques, pages 946–956. Springer International Publishing, Cham, DE.
Kannagi, L., Ramya, C., Shreya, R., and Sowmiya, R. (2018). Virtual conversational assistant:‘the farmbot’. International Journal of Engineering Technology Science and Research, 5(3):520–527.
Lane, H., Howard, C., and Hapke, H. (2019). Natural Language Processing in Action. Manning Publications, New York, NY, USA.
Leitzke, B., Farias, G., Melo, M., Gonçalves, M., Born, M., Rodrigues, P., Martins, V., Barbosa, R., Aguiar, M., and Adamatti, D. (2019). Sistema multiagente para gestão de recursos hídricos: Modelagem da bacia do são gonçalo e da lagoa mirim. In Workshop de Computação Aplicada a Gestão do Meio Ambiente e Recursos Naturais, pages 87– 96, Porto Alegre, RS, Brasil. SBC.
Mikolov, T., Chen, K., Corrado, G. S., and Dean, J. (2013). Efficient estimation of word representations in vector space.
Miñarro-Giménez, J. A., Marín-Alonso, O., and Samwald, M. (2015). Applying deep learning techniques on medical corpora from the world wide web: a prototypical system and evaluation.
Nallappan, M. (2018). A prediction system for farmers to enhance the agriculture yield using cognitive data science. International Journal of Advanced Research in Computer Science, 9:780–784.
Pennington, J., Socher, R., and Manning, C. (2014). GloVe: Global vectors for word representation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543.
Ponte, B., de la Fuente, D., ParreÑo, J., and Pino, R. (2016). Intelligent decision support system for real-time water demand management. International Journal of Computational Intelligence Systems, 9(1):168–183.
Raj, S. (2019). Building Chatbots with Python: Using Natural Language Processing and Machine Learning. Apress, New York, NY, USA.
Sawant, D., Jaiswal, A., Singh, J., and Shah, P. (2019). Agribot - an intelligent interactive interface to assist farmers in agricultural activities. In Proceedings of the IEEE Bombay Section Signature Conference (IBSSC), pages 1–6, Mumbai, India. IEEE.
Publicado
18/07/2021
Como Citar
ALVES, Bruno C.; FREITAS, Larissa A. de; AGUIAR, Marilton S. de.
Chatbot as support to decision-making in the context of natural resource management. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 12. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 29-38.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2021.15734.
