• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1987.10a
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• pp.86-89
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• 1987

• Bulletin of the Society of Naval Architects of Korea
• /
• v.35 no.3
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• pp.50-58
• /
• 1998

• Proceedings of the Korean Society for Applied Microbiology Conference
• /
• 2001.06a
• /
• pp.95-95
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• 2001

• Information and Communications Magazine
• /
• v.20 no.4
• /
• pp.38-48
• /
• 2003

• The Journal of Fisheries Business Administration
• /
• v.1 no.2
• /
• pp.35-47
• /
• 1970

• Proceedings of the Optical Society of Korea Conference
• /
• 2002.11a
• /
• pp.78-79
• /
• 2002

• Korean Architects
• /
• s.655
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• pp.38-41
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• 2023

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.4
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• pp.99-105
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• 2017

Plato and Aristotle examined what the true spirit of physical education is and what kind of relationship it is, from the perspective of dividing into two parts of the body and mind. And the relationship between human body and mind, knowledge and behavior. Plato and Aristotle examined the harmonious combination of body and mind and what is the desirable relationship setting. In Plato's soul and body in philosophical centered ideological aspect, human education center emphasizes poetry education, but physical education is recognized as essential education for human education. Plato's body contour emphasizes the harmony of soul and body, not the superiority of the body to the mind. In Plato's education room, physical education should be preceded and then mental education should be done. I thought that there could be no independent souls without bodies. It is not an independent entity but a unified entity. Because there is a body, there is a soul. There is a soul, so a body exists. Aristotle thought that the body was more important than Plato, and that the body should be preceded by the soul.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.327-329
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• 2017

개체명 인식(Named Entity Recognition) 시스템은 문서에서 인명(PS), 지명(LC), 단체명(OG)과 같은 개체명을 가지는 단어나 어구를 해당 개체명으로 인식하는 시스템이다. 개체명 인식 시스템을 개발하기 위해 딥러닝 기반의 워드 임베딩(word embedding) 자질과 문장의 형태적 특징 및 기구축 사전(lexicon) 기반의 자질 구성 방법을 제안하고, bi-directional LSTM, CNN, CRF과 같은 모델을 이용하여 구성된 자질을 학습하는 방법을 제안한다. 실험 데이터는 2017 국어 정보시스템 경진대회에서 제공한 2016klpNER 데이터를 이용하였다. 실험은 전체 4258 문장 중 학습 데이터 3406 문장, 검증 데이터 426 문장, 테스트 데이터 426 문장으로 데이터를 나누어 실험을 진행하였다. 실험 결과 본 연구에서 제안하는 모델은 BIO 태깅 방식의 개체 청크 단위 성능 평가 결과 98.9%의 테스트 정확도(test accuracy)와 89.4%의 f1-score를 나타냈다.

• Genomics & Informatics
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• v.18 no.2
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• pp.13.1-13.6
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• 2020

The prototype version of the full-text corpus of Genomics & Informatics has recently been archived in a GitHub repository. The full-text publications of volumes 10 through 17 are also directly downloadable from PubMed Central (PMC) as XML files. During the Biomedical Linked Annotation Hackathon 6 (BLAH6), we experimented with converting, annotating, and updating 301 PMC full-text articles of Genomics & Informatics using PubAnnotation, a system that provides a convenient way to add PMC publications based on PMCID. Thus, this review aims to provide a tutorial overview of practicing the iterative task of named entity recognition with the PubAnnotation/PubDictionaries/TextAE ecosystem. We also describe developing a conversion tool between the Genia tagger output and the JSON format of PubAnnotation during the hackathon.