Acknowledgement
This study was supported by a grant from "Development of an artificial intelligence support platform for the development of intelligent railway and transportation technologies" of the Korea Railroad Research Institute's major project (PK2201C1).
References
- D. W. Otter, J. R. Medina, and J. K. Kalita, "A survey of the usages of deep learning for natural language processing," IEEE transactions on neural networks and learning systems, 32(2), pp. 604-624, 2020. DOI: https://doi.org/10.1109/TNNLS.2020.2979670
- S. Bird, "NLTK: the natural language toolkit" in Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions, pp. 69-72, 2006. DOI: https://doi.org/10.48550/arXiv.cs/0205028
- E. L. Park, and S. Cho, "KoNLPy:Korean natural language processing in Python," in Proceedings of the 26th Annual Conference on Human &Cognitive Language Technology, pp. 133-136, 2014.
- A. Aizawa, "An information-theoretic perspective of tf-idf measures," Information Processing & Management, 39(1), pp. 45-65, 2003. DOI: https://doi.org/10.1016/S0306-4573(02)00021-3
- J. Qiang, P. Chen, T. Wang, and X. Wu, "Topic modeling over short texts by incorporating word embeddings," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Cham. pp. 363-374, 2017. DOI: https://doi.org/10.48550/arXiv.1609.08496
- J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding," arXiv preprint arXiv:1810.04805, 2018. DOI: https://doi.org/10.48550/arXiv.1810.04805
- S. Gururangan, A. Marasovic, S. Swayamdipta, K. Lo, I. Beltagy, D. Downey, and N. A. Smith, "Don't stop pretraining: adapt language models to domains and tasks," arXiv preprint arXiv:2004.10964, 2020. DOI: https://doi.org/10.48550/arXiv.2004.10964
- L. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, and J. Kang, "BioBERT: a pre-trained biomedical language representation model for biomedical text mining," Bioinformatics, Volume 36, Issue 4, pp. 1234-1240, 2020. DOI: https://doi.org/10.1093/bioinformatics/btz682
- I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras, and I. Androutsopoulos, "LEGAL-BERT: The Muppets straight out of Law School," in Findings of the Association for Computational Linguistics: EMNLP, 2898 .2904, 2020. DOI: https://doi.org/10.48550/arXiv.2010.02559
- I. Beltagy, K. Lo, and A. Cohan, "SciBERT: A Pretrained Language Model for Scientific Text," in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3615.3620, 2019. DOI: https://doi.org/10.48550/arXiv.1903.10676
- D. Kim, D. Lee, J. Park, S. Oh, S. Kwon, I. Lee, and D. Choi, "KB-BERT: Trainning and Appliocation of Korea Pre-trained Language Model in Financial Domain,", Journal of Intelligence and Information Systems, Vol. 28, No. 2, pp. 191-206, 2022. DOI: https://dx.doi.org/10.13088/jiis.2022.28.2.191
- C.W. Park, and J.H. Song, "A Study on the Establishment of an Annotation System for Text-Based Cultural Heritage,", Journal of the Korea Academia-Industrial cooperation Society, Vol. 22, No. 11, pp. 754-759, 2021. DOI: http://doi.org/10.5762/KAIS.2021.22.11.754