참고문헌
- K. Kowsari, D. E. Brown, M. Heidarysafa, K. J. Meimandi, M. S. Gerber, and L. E. Barnes, "HDLTex: Hierarchical deep learning for text classification," 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.364-371, 2017. doi: 10.1109/ICMLA.2017.0-134.
- C. C. Aggarwal and C. X. Zhai, "A survey of text classification algorithms," Mining Text Data, pp.163-222, Aug. 2012. doi: 10.1007/978-1-4614-3223-4_6.
- J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.4171-4186, 2018, Accessed: Dec. 27, 2021. [Online]. Available: https://arxiv.org/abs/1810.04805v2.
- SKTBrain/KoBERT: Korean BERT pre-trained cased (KoBERT) [Internet], https://github.com/SKTBrain/KoBERT (accessed Dec. 27, 2021).
- M. K. M. Boussougou, S. Jin, D. Chang, and D.-J. Park, "Korean voice phishing text classification performance analysis using machine learning techniques," Proceedings of the Korea Information Processing Society Conference, pp. 297-299, 2021. doi: 10.3745/PKIPS.Y2021M11A.297.
- K. Kowsari, K. J. Meimandi, M. Heidarysafa, S. Mendu, L. E. Barnes, and D. E. Brown, "Text classification algorithms: A survey," Information (Switzerland), Vol.10, No.4, Apr. 2019, doi: 10.3390/info10040150.
- M. K. M. Boussougou and D.-J. Park, "A real-time efficient detection technique of voice phishing with AI," in Proceedings of Korea Software Congress 2021, pp.768-770, 2021, Accessed: Oct. 13, 2021. [Online]. Available: https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE10583070.
- Catboost, "CatBoost - open-source gradient boosting library," 2021. [Internet], https://catboost.ai/ (accessed Mar. 11, 2022).
- XBGoost, "XGBoost Documentation - Introduction to Boosted Trees," 2020. [Internet], https://xgboost.readthedocs.io/en/latest/tutorials/model.html (accessed Mar. 11, 2022).
- T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.785794, 2016. doi: 10.1145/2939672.2939785.
- G. Ke et al., "LightGBM: A highly efficient gradient boosting decision tree," Advances in Neural Information Processing Systems, pp.3147-3155, 2017, Accessed: Mar. 11, 2022. [Online]. Available: https://github.com/Microsoft/LightGBM.
- "Welcome to LightGBM's documentation! - LightGBM 3.3.2.99 documentation." [Internet], https://lightgbm.readthedocs.io/en/latest/index.html (accessed Mar. 11, 2022).
- Z. C. Lipton, J. Berkowitz, and C. Elkan, "A critical review of recurrent neural networks for sequence learning," 2015, Accessed: Mar. 11, 2022. [Online]. Available: http://arxiv.org/abs/1506.00019.
- A. Sherstinsky, "Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network," Physica D: Nonlinear Phenomena, Vol.404, 2020, doi: 10.1016/j.physd.2019.132306.
- M. Schuster and K. K. Paliwal, "Bidirectional recurrent neural networks," IEEE Transactions on Signal Processing, Vol.45, No.11, pp.2673-2681, 1997. doi: 10.1109/78.650093.
- A. Graves and J. Schmidhuber, "Framewise phoneme classification with bidirectional LSTM and other neural network architectures," Neural Networks, Vol.18, No.5-6, pp.602-610, 2005, doi: 10.1016/j.neunet.2005.06.042.
- K. Cho et al., "Learning phrase representations using RNN encoder-decoder for statistical machine translation," EMNLP 2014 - 2014 In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1724-1734, 2014. doi: 10.3115/v1/d14-1179.
- H. Cho, H. Im, Y. Yi, and J. Cha, "Comparison of Korean classification models' Korean essay score range prediction performance," KIPS Transactions on Software and Data Engineering, Vol.11, No.3, pp.133-140, 2022. doi: 10.3745/KTSDE.2022.11.3.133.
- S. Choi, M.-K. Park, and E. Kim, "How are Korean neural language models 'surprised' layerwisely?," Journal of Language Sciences, Vol.28, No.4, pp.301-317, 2021. doi: 10.14384/kals.2021.28.4.301.
- K. Yang, "Transformer-based Korean pretrained language models: A survey on three years of progress," 2021, Accessed: Mar. 11, 2022. [Online]. Available: http://arxiv.org/abs/2112.03014.
- K. Yang, W. Jang, and W. I. Cho, "APEACH: Attacking pejorative expressions with analysis on crowd-generated hate speech evaluation datasets," 2022, Accessed: Mar. 13, 2022. [Online]. Available: http://arxiv.org/abs/2202.12459.
- S. Park, H. Yang, M. Choe, M. Ha, K. Chung, and M. Koo, "Sentimental Analysis of YouTube Korean Comments Using KoBERT," in Proceedings of Korea Software Congress 2020, pp.1385-1387, 2020. [Online]. Available: http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE10529995
- K.-H. Park, Y.-S Jeong, "Korean daily conversation topics classification using KoBERT," in Proceedings of Korea Software Congress 2021, pp.1735-1737, 2021. Accessed: Dec. 27, 2021. [Online]. Available: https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE10583420.
- A. K. Uysal and S. Gunal, "The impact of preprocessing on text classification," Information Processing & Management, Vol.50, No.1, pp.104-112, 2014. doi: 10.1016/J.IPM.2013.08.006.
- E. Grave, P. Bojanowski, P. Gupta, A. Joulin, and T. Mikolov, "Learning word vectors for 157 languages," 2019. Accessed: Nov. 15, 2020. [Online]. Available: https://fasttext.cc/