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NTIS 바로가기정보관리학회지 = Journal of the Korean society for information management, v.35 no.4 = no.110, 2018년, pp.141 - 164
김선우 (경기대학교 문헌정보학과) , 고건우 (경기대학교 문헌정보학과) , 최원준 (한국과학기술정보연구원 콘텐츠큐레이션센터) , 정희석 (한국과학기술정보연구원 콘텐츠큐레이션센터) , 윤화묵 (한국과학기술정보연구원 콘텐츠큐레이션센터) , 최성필 (경기대학교 문헌정보학과)
Recently, as the amount of academic literature has increased rapidly and complex researches have been actively conducted, researchers have difficulty in analyzing trends in previous research. In order to solve this problem, it is necessary to classify information in units of academic papers. However...
Kim, Seon-Wu, & Choi, Sung-Pil (2018). Research on joint models for korean word spacing and POS tagging based on bidirectional LSTM-CRF. Journal of Information Science, 45(8), 792-800.
Kim, Pan-Jun, & Lee, Jae-Yun (2014). An experimental study on the performance improvement of automatic classification for the articles of korean journals based on controlled keywords in international database. Journal of the Korean Society for Library and Information Science, 48(3), 491-510. https://doi.org/10.4275/KSLIS.2014.48.3.491
Ra. Dong-Yul, Kang, Hyun-Kyu, Kim, Hyun-Tae, Park, Kyung-Il, Jang, Hyeong-Il, Yeom, Sung-Wook, ... & Shin, Hyun-Ju (2007). Development of a test collection HANTEC for evaluating information retrieval.management.service. (report no. K-07-IP-02-03S-7). Korea Institute of Science and Technology Information.
Ra, Dong-Yul, Kim, Yun-Sik, Shin, Hyun-Joo, Lee, Kyu-Hee, Kim, Tae-Kyu, Kang, Hyun-Kyu, ... & Yoon, Hwa-Mook (2007). Developing a test collection for korean text categorization. Proceedings of the Korea Contents Association Conference, 5(1), 435-439.
Noh, Dae-Wook, Lee, Soo-Yong, & Ra, Dong-Yul (2007). Developing a text categorization system based on unsupervised learning using an information retrieval technique. Information Science Journal: Software and Application, 34(2), 160-168.
Park, Young-Keun, Park, Su-Bin, Park, No-il, & Lee, Hyun-Ah (2017). Web news classification using latent semantic analysis. Korea Information Science Society Academic Conference Academic Literature, 1828-1830.
Lee, Da-Bin, & Choi, Sung-Pil (2018). In-depth comparative analysis of various korean morpheme embedding models using massive textual resource. Korea Information Science Society Academic Conference Academic Literature, 613-615.
Cho, Hyun-Soo, & Lee, Sang-Goo (2017). Korean word embedding using fasttext. Korea Information Science Society Academic Conference Academic Literature, 705-707.
Cho, Hui-Yeol, Kim, Jin-Hwa, Yoon, Sang-Woong, Kim, Kyung-Min, & Zhang, Byung-Tak (2015). Large-scale text classification methodology with convolutional neural network. Korea Information Science Society Academic Conference Academic Literature, 792-794.
Choi, Sung-Pil, Yoo, Suk-Jong, & Cho, Hyun-Yang (2016). A study on the semiautomatic construction of domain-specific relation extraction datasets from biomedical abstracts - Mainly focusing on a genic interaction dataset in alzheimer's disease domain -. Journal of Korean Library and Information Science Society, 47(4), 289-307. https://doi.org/10.16981/kliss.47.4.201612.289
Han, Kyu-Yeol, & Ahn, Young-Min (2013). Automatic labeling of korean document clusters created by LDA. Journal of Korean Society of Information Science. Korea Information Science Society Academic Conference Academic Literature, 616-618.
Bock, H. H. (2007). Clustering methods: a history of k-means algorithms. In Selected contributions in data analysis and classification, 161-172. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73560-1_15
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2016). Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606.
Choi, S. P. (2018). Extraction of protein-protein interactions (PPIs) from the literature by deep convolutional neural networks with various feature embeddings. Journal of Information Science, 44(1), 60-73. https://doi.org/10.1177/0165551516673485
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016). Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759.
Kowsari, K., Brown, D. E., Heidarysafa, M., Meimandi, K. J., Gerber, M. S., & Barnes, L. E. (2017, December). Hdltex: Hierarchical deep learning for text classification. In Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on, 364-371. https://doi.org/10.1109/ICMLA.2017.0-134
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, 3111-3119.
Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532-1543. http://dx.doi.org/10.3115/v1/D14-1162
Shafiabady, N., Lee, L. H., Rajkumar, R., Kallimani, V. P., Akram, N. A., & Isa, D. (2016). Using unsupervised clustering approach to train the support vector machine for text classification. Neurocomputing, 211, 4-10. https://doi.org/10.1016/j.neucom.2015.10.137
Shinyama, Y. (2004). PDFMiner. Retrieved from https://euske.github.io/pdfminer/
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