References
- J. T. Kim & H. G. Lee & H. S. Kim. (2020). Effective Generative Chatbot Model Trainable with a Small Dialogue Corpus. Journal of Korean Institute of Information Scientists and Engineers, 46(3), 246-252. DOI : 10.5626/JOK.2019.46.3.246
- D. A. Park. (2017). A Study on Conversational Public Administration Service of the Chatbot Based on Artificial Intelligence. Journal of Korea Mutimedia Society, 20(8), 1347-1356 DOI : I410-ECN-0101-2018-004-001287355
- M. J. Kang. (2018). A Study of Chatbot Personality based on the Purposes of Chatbot. Journal of the Korea Contents Association, 18(5), 319-329. DOI : I410-ECN-0101-2018-310-002251103 https://doi.org/10.5392/JKCA.2018.18.05.319
- J. J. Kim & H. J. Jo. (2019). Development of Conversational News Chatbot System Based on User Intent Analysis. Journal of Digital Contents Society, 20(5), 963-972. DOI : 10.9728/dcs.2019.20.5.963
- M. C. Sung. (2020). Pre-Service Primary English Teachers' AI Chatbots. Journal of Language Research, 56(1), 97-115. DOI : 10.9728/dcs.2019.20.2.241
- S. H. Choi & J. Y. Kim & J. H. Song & S. M. Jung & S. J. Hong. (2019). Labor Law Consulting System With IBM Watson Chatbot. Journal of Digital Contents Society, 20(2), 241-249. DOI : 10.9728/dcs.2019.20.2.241
- J. W. Kim & H. I. Jo & B. G. Lee. (2019). The Study on the Factors Influencing on the Behavioral Intention of Chatbot Service for the Financial Sector - Focusing on the UTAUT Model. Journal of Digital Contents Society, 20(1), 41-50. DOI : 10.9728/dcs.2019.20.1.41
- X. F. Wang & H. C. Kim. (2018). Text Categorization with Improved Deep Learning Methods. Journal of Information and Communication Convergence Engineering, 16(2), 106-113. DOI : 10.6109/jicce.2018.16.2.106
- D. H. Seo & J. S. Lyu & E. J. Choi & S. H. Cho & D. K. Kim. (2018). Web based Customer Power Demand Variation Estimation System using LSTM. Journal of the Korea Institute of Information and Communication Engineering, 22(4), 587-594. DOI : 10.6109/jkiice.2018.22.4.587
- J. W. Lee & H. Y. Kim & H. K. Jung. (2020). Deep Learning Module Optimization based on Sequential Data Prediction. ASM Science Journal, 13(1), 82-91.
- Y. H. Kim & Y. K. Hwang & T. G. Kang & K. M. Jung. (2016). LSTM Language Model Based Korean Sentence Generation. The Journal of Korean Institute of Communications and Information Sciences, 41(5), 592-601. DOI : 10.7840/kics.2016.41.5.592
- I. T. Joo & S. H. Choi. (2018). Stock Prediction Model based on Bidirectional LSTM Recurrent Neural Network. Journal of Korea institute of information, electronics, and communication technology, 11(2), 204-208. DOI : 10.17661/jkiiect.2018.11.2.204
- H. I. Kim & J. Y. Lee. (2020). Prediction of Urban Flood Extent by LSTM Model and Logistic Regression. Journal of the Korean Society of Civil Engineers, 40(3), 273-283. DOI : 10.12652/Ksce.2020.40.3.0273
- T. H. Min & H. J. Shin & J. S. Lee. (2019). Korean Spatial Information Extraction using Bi-LSTM-CRF Ensemble Model. The Journal of the Korea Contents Association, 19(11), 278-287. DOI : 10.5392/JKCA.2019.19.11.278
- H. Y. Yu & Y. J. Ko. (2017). Expansion of Word Representation for Named Entity Recognition Based on Bidirectional LSTM CRFs. Journal of Korean Institute of Information Scientists and Engineers, 44(3), 306-313. DOI : 10.5626/JOK.2017.44.3.306