A chatbot using Levenshtein distance algorithm for Raspberry board
Corressponding author's email:
sontn@hcmute.edu.vnKeywords:
Chatbox, Levenshtein distance, Search algorithm, Neural network, Convolutional neural networksAbstract
In this paper, we present a chatbot based on the Levenshtein Distance for low-cost embedded systems. The state-of-the art chatbots are based on deep neural networks, however, such chatbots cannot be deployed on the low-cost embedded system, such as Raspberry board for mobile robots. Chatbot based on Levenshtein Distance requires fewer resources and can be deployed on low-cost embedded systems efficiently. The Levenshtein distance represents the similarity between the two strings. The similarity between the input question and all the stored questions in the database are measured. A winner is a stored question that is the best similar to the input question. Having recognized the question, chatbot can decide the output by querying from the database. Chatbot using (a) search algorithm based on Levenshtein distance is faster by 15 times and 75 times than the Convolutional Neural Network and the LSTM network. The chatbot based on Levenshtein Distance is suitable to be deployed on the low-cost embedded systems for mobile robots.
Downloads: 0
References
B. A. Shawar and E. Atwell, “Different measurement metrics to evaluate a chatbot system,” in Proceedings of the workshop on bridging the gap: Academic and industrial research in dialog technologies, 2007, pp. 89–96.
A. M. Rahman, A. Al Mamun, and A. Islam, “Programming challenges of chatbot: Current and future prospective,” in 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 2017, pp. 75–78.
J.-C. Gu, Z.-H. Ling, and Q. Liu, “Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots,” IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 28, pp. 369–379, 2020.
B. Setiaji and F. W. Wibowo, “Chatbot Using a Knowledge in Database: Human-to-Machine Conversation Modeling,” in 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), 2016, pp. 72–77.
G. M. D’silva, S. Thakare, S. More, and J. Kuriakose, “Real world smart chatbot for customer care using a software as a service (SaaS) architecture,” in 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2017, pp. 658–664.
M. Bates, “Health Care Chatbots Are Here to Help,” IEEE Pulse, vol. 10, no. 3, pp. 12–14, May 2019.
D. Madhu, C. J. N. Jain, E. Sebastain, S. Shaji, and A. Ajayakumar, “A novel approach for medical assistance using trained chatbot,” in 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), 2017, pp. 243–246.
A. Mondal, M. Dey, D. Das, S. Nagpal, and K. Garda, “Chatbot: An automated conversation system for the educational domain,” in 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), 2018, pp. 1–5.
M. Nuruzzaman and O. K. Hussain, “A Survey on Chatbot Implementation in Customer Service Industry through Deep Neural Networks,” in 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE), 2018, pp. 54–61.
H. Honda and M. Hagiwara, “Question Answering Systems With Deep Learning-Based Symbolic Processing,” IEEE Access, vol. 7, pp. 152368–152378, 2019.
A. Ene and A. Ene, “An application of Levenshtein algorithm in vocabulary learning,” in 2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2017, pp. 1–4.
G. Navarro, “A guided tour to approximate string matching,” ACM Comput. Surv., vol. 33, no. 1, pp. 31–88, Mar. 2001.
V. I. Levenshtein, “Binary codes capable of correcting deletions, insertions, and reversals,” in Soviet physics doklady, 1966, vol. 10, no. 8, pp. 707–710.
A. Andoni, R. Krauthgamer, and K. Onak, “Polylogarithmic Approximation for Edit Distance and the Asymmetric Query Complexity,” in Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS, 2010, pp. 244–252.
D. Q. Thang and P. T. Huy, “Determining restricted Damerau-Levenshtein editdistance of two languages by extended automata,” in 2010 IEEE-RIVF International Conference on Computing and Communication Technologies: Research, Innovation and Vision for the Future, RIVF 2010, 2010.
K. U. Schulz and S. Mihov, “Fast string correction with Levenshtein automata,” Int. J. Doc. Anal. Recognit., vol. 5, no. 1, pp. 67–85, Nov. 2002.
X. Wen and Y. Wang, “Design of smart home environment monitoring system based on raspberry Pi” 2018 Chinese Control And Decision Conference (CCDC), Shenyang, 2018, pp. 4259-4263.
S. Jain, A. Vaibhav and L. Goyal, “Raspberry Pi based interactive home automation system through E-mail,” 2014 International Conference on Reliability Optimization and Information Technology (ICROIT), Faridabad, 2014, pp. 277-280.
Downloads
Published
How to Cite
Issue
Section
Categories
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright © JTE.
