Abstract
The combination of commercial development and artificial intelligence services becomes more and more important. Chatbot is considered one of the effective techniques by using information retrieval (IR) and natural language processing (NLP). In this paper, we collect a series of business-promoted chat data and conduct a series of cleanup and classification of these data sets. Since the speech of different people is random, the similarity is calculated by using a combination of the retrieval model and the generated model, and then the final answer is generated using long-short term memory (LSTM) training and prediction. Finally, we use the TF-IDF weighting method to improve the dialog. Experimental results show that the proposed method can communicate with humans and answer real-time questions.
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References
Vargas-Vera, M., Lytras, M.D.: AQUA: hybrid architecture for question answering services. IET Softw. 4(6), 418–433 (2010)
Abdul-kader, S.A.: Question answer system for online feedable new born chatbot. In: 2017 Intelligent Systems Conference (IntelliSys), pp. 863–869 (2017)
Su, M.-H., Wu, C.-H., Huang, K.-Y., Hong, Q.-B., Wang, H.-M.: A chatbot using LSTM-based multi-layer embedding for elderly care. In: 2017 International Conference on Orange Technologies (ICOT), pp. 70–74 (2017)
Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. In: Advances in Neural Information Processing Systems, vol. 27, pp. 2042–2050 (2014)
Quarteroni, S., Manandhar, S.: Designing an interactive open-domain question answering system. Nat. Lang. Eng. 15(1), 73–95 (2009)
Grangier, D., Keshet, J., Bengio, S.: Discriminative keyword spotting. Speech Commun. 51(4), 317–329 (2009)
Spärck Jones, K.: A statistical interpretation of term specificity and its retrieval. J. Doc. 28(1), 11–21 (1972)
Guo, A., Yang, T.: Research and improvement of feature words weight based on TFIDF algorithm. In: Proceedings of 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2016), pp. 415–419 (2016)
Hochreiter, S., Schmidhuber, J.J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Graves, A., Jaitly, N.: Towards end-to-end speech recognition with recurrent neural networks. In: International Conference on Machine Learning, pp. 1764–1772 (2014)
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Zhang, J., Huang, H., Gui, G. (2020). A Chatbot Design Method Using Combined Model for Business Promotion. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-13-6508-9_137
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DOI: https://doi.org/10.1007/978-981-13-6508-9_137
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