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22대 대한민국 국회 국회정보길라잡이 국회의원검색
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Korean is an agglutinative language, and one or more morphemes are combined to form a single word. Part-of-speech tagging method separates each morpheme from a word and attaches a part-of-speech tag. In this study, we propose a new Korean part-of-speech tagging method based on the Head-Tail tokenization technique that divides a word into a lexical morpheme part and a grammatical morpheme part without decomposing compound words. In this method, the Head-Tail is divided by the syllable boundary without restoring irregular deformation or abbreviated syllables. Korean part-of-speech tagger was implemented using the Head-Tail tokenization and deep learning technique. In order to solve the problem that a large number of complex tags are generated due to the segmented tags and the tagging accuracy is low, we reduced the number of tags to a complex tag composed of large classification tags, and as a result, we improved the tagging accuracy. The performance of the Head-Tail part-of-speech tagger was experimented by using BERT, syllable bigram, and subword bigram embedding, and both syllable bigram and subword bigram embedding showed improvement in performance compared to general BERT. Part-of-speech tagging was performed by integrating the Head-Tail tokenization model and the simplified part-of-speech tagging model, achieving 98.99% word unit accuracy and 99.08% token unit accuracy. As a result of the experiment, it was found that the performance of part-of-speech tagging improved when the maximum token length was limited to twice the number of words.

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기사명 저자명 페이지 원문 목차
비디오 영상에서 2차원 자세 추정과 LSTM 기반의 행동 패턴 예측 알고리즘 = Behavior pattern prediction algorithm based on 2D pose estimation and LSTM from videos 최지호, 황규태, 이상준 p. 191-197

딥러닝을 이용한 한국어 Head-Tail 토큰화 기법과 품사 태깅 = Korean Head-Tail tokenization and part-of-speech tagging by using deep learning 김정민, 강승식, 김혁만 p. 199-208

비지도학습 기반의 뎁스 추정을 위한 지식 증류 기법 = Knowledge distillation for unsupervised depth estimation 송지민, 이상준 p. 209-215

Cycle-accurate NPU 시뮬레이터 및 데이터 접근 방식에 따른 NPU 성능평가 = Cycle-accurate NPU simulator and performance evaluation according to data access strategies 권구윤, 박상우, 서태원 p. 217-228

웨어러블 센서를 활용한 경량 인공신경망 기반 손동작 인식기술 = A light-weight ANN-based hand motion recognition using a wearable sensor 이형규 p. 229-237

GNSS 부분 음영 지역에서 마할라노비스 거리를 이용한 GNSS/다중 IMU 센서 기반 측위 알고리즘 = GNSS/multiple IMUs based navigation strategy using the Mahalanobis distance in partially GNSS-denied environments 김지연, 송무근, 김재훈, 이동익 p. 239-247

다중 선형 회귀와 랜덤 포레스트 기반의 코로나19 신규 확진자 예측 = Prediction of new confirmed cases of COVID-19 based on multiple linear regression and random forest 김준수, 최병재 p. 249-255

참고문헌 (28건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 D. H. Park, W. S. Choi, H. J. Kim, S. L. Lee, “Web Document Classification Based on Hangeul Morpheme and Keyword Analyses”, Journal of Korean Information Processing Society D, Vol. 19, No. 4, pp. 263-270, 2012 미소장
2 W. Cho, G. Shin, W. Lee, S. Son, H. Song, J. Lee, H. Lee, S. Jo, “KoELECTRA-Based Named Entity Recognition Using Korean Morphological Analyzers”, Proceedings of 2021 Korea Computer Congress, pp. 1897-1899, 2021 미소장
3 Y. Ha, J. Cheon, I. Wang, M. Park, G. Woo, “A Filtering Method of Malicious Comments Through Morpheme Analysis”, Journal of Korea Contents Association, Vol. 21, No. 9, pp. 750-761, 2021. 미소장
4 S. S. Kang, “Analysis of Korean Irregular Verbs by using Syllable Characteristics”, Proceedings of 5th Hangul and Korean Information Processing, pp. 385-394, 1993. 미소장
5 S. S. Kang, “Multi-level Morphology and Morphological Analysis Model for Korean”, Proceedings of 6th Hangul and Korean Information Processing, pp. 140-145, 1994 미소장
6 J. S. Lee, “Three-Step Probabilistic Model for Korean Morphological Analysis”, Journal of KIISE, Vol. 38, No. 5, pp. 257-268, 2011. 미소장
7 D. Bahdanau, K. Cho, Y. Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate”, ICLR 2015, arXiv:1409.0473, 2015. 미소장
8 A. Vaswan, N. Shazeer., N. Parmar., J. Uszkoreit., L.Jones.,N. Aidan. Gomez, L. Kaiser., L. Polosukhin., “Attention Is All You Need”, Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS), arXiv:1706.03762v5, 2017. 미소장
9 J. Devlin., M. W. Chang., K. Lee., K. Toutanova., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, preprint arXiv:1810.04805, 2018. 미소장
10 H. Y. Lee, S. S. Kang, “Bi-LSTM-CRF and Syllable Embedding for Automatic Spacing of Korean Sentences”, Proceedings of 30th Hangul and Korean Information Processing, pp. 605-607, 2018 . 미소장
11 K. S. Shim, “Automatic Word Spacing based on Conditional Random Fields”, Korean Journal of Cognitive Science, Vol. 22, No. 2, pp. 217-233, 2011 . 미소장
12 H. Y. Lee, S. S. Kang, “Compound Noun Decomposition by using Syllable-based Embedding and Deep Learning”, Journal of Smart Media, Vol. 8, No. 2, pp. 74-79, 2019 미소장
13 J. H. Shin, H. R. Park, “A Statistical Model for Korean Text Segmentation by using Syllable-level Bigrams”, Proceedings of 9th Hangul and Korean Information Processing, pp. 255-260, 1997 미소장
14 S. S. Kang, “Automatic Correction of Word-spacing Errors by using Syllable Bigram”, Journal of Speech Science, Vol. 8, No. 2, pp. 83-90, 2001. 미소장
15 D. H. Lim, Y. J. Chun, H. J. Kim, S. S. Kang, “Word Segmentation by using Extended Syllable Bigram”, Proceedings of 17th Hangul and Korean Information Processing, pp. 189-193, 2005 . 미소장
16 T. Kudo.., “Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates”, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 66-75, 2018. 미소장
17 T. Kudo, J. Richardson., “SentencePiece: A Simple and Language Independent Subword Tokenizer nd Detokenizer for Neural Text Processing”, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System, pp. 66-71. 2018. 미소장
18 J. Lafferty, A. McCallum, F. C. N. Pereira, “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data”, Proceedings of the 18th International Conference on Machine Learning 2001, pp. 282-289, 2001. 미소장
19 T. Brants., “TnT - A Statistical Part-of-Speech Tagger”, Proceedings of the 6th Applied Natural Language Processing Conference, pp. 224-231, 2000. 미소장
20 J. Youn, J. Lee, “A Deep Learning-based Two-Steps Pipeline Model for Korean Morphological Analysis and Part-of-Speech Tagging”, Journal of KIISE, Vol. 48, No. 4, pp. 444-452, 2021. 미소장
21 J. Li, E. H. Lee, J. H. Lee, “Sequence-to-sequence based Morphological Analysis and Part-Of-Speech Tagging for Korean Language with Convolutional Features”, Journal of KIISE, Vol. 44, No. 1, pp. 57-62, 2017 . 미소장
22 Y. Choi, K. Lee, “Performance Analysis of Korean Morphological Analyzer based on Transformer and BERT”, Journal of KIISE, Vol. 47, No. 8, pp. 730-741, 2020 미소장
23 J. M. Kim, H. J. Seo, S. S. Kang, “Korean Head-Tail POS-Tagger by using Transformer”, Proceedings of 33th Hangul and Korean Information Processing, pp. 544-547, 2021. 미소장
24 D. Kim, Y. Bae, C. Ock, H. Choi, C. Kim, “Korean POS and Homonym Tagging System using HMM”, Proceedings of 20th Hangul and Korean Information Processing, pp. 12-16, 2008 미소장
25 J. H. Kim, “Korean Part-of-Speech Tagging using a Weighted Network”, Journal of KIISE, Vol. 25, No. 6, pp. 951-959, 1998 미소장
26 K. S. Shim, “Syllable-based POS Tagging without Korean Morphological Analysis”, Korean Journal of Cognitive Science, Vol. 22, No. 3, pp. 327-345, 2011 미소장
27 J. M. Kim, H. J. Seo, S. S. Kang, “Korean Head-Tail POS-Tagger by using Transformer”, Journal of Smart Media, Vol. 11, No. 5, pp. 17-25, 2022 미소장
28 D. Cho, “Hate Speech Detection by using Word Embedding and Deep Learning Model”, M.S. Thesis, Kookmin University, 2021 미소장

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