Considerations for Applying Korean Natural Language Processing Technology in Records Management (기록관리 분야에서 한국어 자연어 처리 기술을 적용하기 위한 고려사항)

KIM HAK LAE (김학래)

doi:10.14404/JKSARM.2022.22.4.129

Considerations for Applying Korean Natural Language Processing Technology in Records Management

Journal of Korean Society of Archives and Records Management
Abbr : JRMASK
2022, 22(4), pp.129~149
DOI : 10.14404/JKSARM.2022.22.4.129
Publisher : Korean Society of Archives and Records Management
Research Area : Interdisciplinary Studies > Library and Information Science > Archival Studies / Conservation
Received : October 21, 2022
Accepted : November 25, 2022
Published : November 30, 2022

KIM HAK LAE ¹

¹중앙대학교

Accredited

ABSTRACT

Records have temporal characteristics, including the past and present; linguistic characteristics not limited to a specific language; and various types categorized in a complex way. Processing records such as text, video, and audio in the life cycle of records’ creation, preservation, and utilization entails exhaustive effort and cost. Primary natural language processing (NLP) technologies, such as machine translation, document summarization, named-entity recognition, and image recognition, can be widely applied to electronic records and analog digitization. In particular, Korean deep learning–based NLP technologies effectively recognize various record types and generate record management metadata. This paper provides an overview of Korean NLP technologies and discusses considerations for applying NLP technology in records management. The process of using NLP technologies, such as machine translation and optical character recognition for digital conversion of records, is introduced as an example implemented in the Python environment. In contrast, a plan to improve environmental factors and record digitization guidelines for applying NLP technology in the records management field is proposed for utilizing NLP technology.

KEYWORDS

Records management, Natural language processing, Artificial intelligence, Machine learning, Deep learning

Citation status

* References for papers published after 2023 are currently being built.

[report] 강민균 / 2021 / 빅데이터·인공지능 기술을 활용한 대학 기록관리 표준모델 연구 / 국가기록원

[report] 강지홍 / 2021 / 기록물 OCR 데이터셋 구축 및 한글 필기체 인식 성능 개선 연구 / 국가기록원

[journal] 강형석 / 2019 / Optimization of Word2vec Models for Korean Word Embeddings / 디지털콘텐츠학회논문지 / 한국디지털콘텐츠학회 20(4) : 825~833

[other] / 기록관리 메타데이터 표준(v2.1). NAK/S 8:2016(v2.1)

[other] / 기록물 디지털화 지침-제1부: 종이 및 사진·필름(v1.0). NAK/G 8-1:2013(v1.0)

[other] / 기록물 디지털화 지침. NAK 26:2018(v2.0)

[other] / 기록물 목록 작성 및 디지털화 작업 지침(v1.2). NAK 23:2017(v1.2)

[journal] 김상운 / 2021 / GPT-2 for Knowledge Graph Completion / 정보과학회논문지 / 한국정보과학회 48(12) : 1281~1288

[journal] 김영민 / 2020 / KorQuAD 2.0: Korean QA Dataset for Web Document Machine Comprehension / 정보과학회논문지 / 한국정보과학회 47(6) : 577~586

[journal] 김인택 / 2017 / Intelligent Records and Archives Management That Applies Artificial Intelligence / 한국기록관리학회지 / 한국기록관리학회 17(4) : 225~250

[journal] 김인후 / 2022 / Automatic Classification of Academic Articles Using BERT Model Based on Deep Learning / 정보관리학회지 / 한국정보관리학회 39(3) : 293~310

[journal] 김태영 / 2018 / A Study on the Current Status and Application Strategies for Intelligent Archival Information Services / 한국기록관리학회지 / 한국기록관리학회 18(4) : 149~182

[report] 김학래 / 2022 / 머신러닝 기술을 활용한 국회전자도서관 지식검색 서비스 모델 연구 / 국회도서관

[journal] 김해찬솔 / 2017 / A Study on Automatic Classification of Record Text Using Machine Learning / 정보관리학회지 / 한국정보관리학회 34(4) : 321~344

[report] 류한조 / 2021 / 빅데이터 분석기술을 활용한 자치단체 기록관리기준표 현황분석 및 개선방안 연구 / 국가기록원

[other] / 문헌정보 — 기록관리 — 제1부: 개념과 원칙. KS X ISO15489-1

[journal] 민진우 / 2022 / LUKE for Korean Natural Language Processing: Named Entity Recognition and Entity Linking / 정보과학회 컴퓨팅의 실제 논문지 / 한국정보과학회 28(3) : 175~183

[journal] 박상언 / 2021 / Analysis of the Status of Natural Language Processing Technology Based on Deep Learning / 한국빅데이터학회 학회지 / 사)한국빅데이터학회 6(1) : 63~81

[confproc] 박은정 / 2014 / KoNLPy : 쉽고 간결한 한국어 정보처리 파이썬 패키지 / 제 26회 한글 및 한국어 정보처리 학술대회논문집 2014 : 133~166

[journal] 박진호 / 2019 / The Role of Domain Knowledge in Deep Learning-Based Natural Language Processing / 인공지능인문학연구 / 인문콘텐츠연구소 4 : 134~166

[report] 오효정 / 2019 / 지능형 전자기록관리 기술연구 개발 기획연구 / 국가기록원

[report] 오효정 / 2021 / 기록관리 AI 기술적용을 위한 공통 학습데이터 세트 구축 연구 / 국가기록원

[journal] 유소엽 / 2019 / An Intelligent Chatbot Utilizing BERT Model and Knowledge Graph / 한국전자거래학회지 / 한국전자거래학회 24(3) : 87~98

[journal] 조희련 / 2021 / Automatic Score Range Classification of Korean Essays Using Deep Learning-based Korean Language Models -The Case of KoBERT & KoGPT2- / 한국언어문화학 / 국제한국언어문화학회 18(1) : 217~241

[journal] 최용석 / 2020 / Performance Analysis of Korean Morphological Analyzer based on Transformer and BERT / 정보과학회논문지 / 한국정보과학회 47(8) : 730~741

[journal] 황상흠 / 2020 / BERT-based Classification Model for Korean Documents / 한국전자거래학회지 / 한국전자거래학회 25(1) : 203~214

[other] Alsentzer, E. / 2019 / Publicly available clinical BERT embeddings / arXiv preprint arXiv : 1904

[other] Arefieva, V. / 2022 / TourBERT : A pretrained language model for the tourism industry / arXiv preprint arXiv:2201.07449

[journal] Badrinarayanan, V. / 2017 / Segnet : A deep convolutional encoder-decoder architecture for image segmentation / IEEE transactions on pattern analysis and machine intelligence 39(12) : 2481~2495

[other] Beltagy, I. / 2019 / SciBERT : A pretrained language model for scientific text / arXiv preprint arXiv:1903.10676

[journal] Bengio, Y. / 2021 / Deep learning for AI / Communications of the ACM 64(7) : 58~65

[journal] Bojanowski, P. / 2017 / Enriching word vectors with subword information / Transactions of the association for computational linguistics 5 : 135~146

[confproc] Camacho-Collados, J. / 2020 / Embeddings in natural language processing / Proceedings of the 28th international conference on computational linguistics: tutorial abstracts : 10~15

[journal] Ding, J. / 2020 / Assessment of Empirical Troposphere Model GPT3 Based on NGL's Global Troposphere Products / Sensors 20(13) : 3631~

[journal] Dwivedi, Y. K. / 2022 / Metaverse beyond the hype : Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy / International Journal of Information Management 66

[other] Ethayarajh, K / 2019 / How contextual are contextualized word representations? comparing the geometry of BERT, ELMo, and GPT-2 embeddings / arXiv preprint arXiv:1909.00512

[journal] Floridi, L. / 2020 / GPT-3 : Its nature, scope, limits, and consequences / Minds and Machines 30(4) : 681~694

[confproc] Gonzalez-Dominguez, J. / 2014 / Automatic language identification using long short-term memory recurrent neural networks / Proceedings of the 15th Annual Conference of the International Speech Communication Association (INTERSPEECH) : 2155~2159

[book] Kaplan, J / 2017 / 제리 카플란 인공지능의미래 : 상생과 공존을 위한 통찰과 해법들 / 한즈미디어

[book] LeCun, Y. / 1995 / The handbook of brain theory and neural networks

[journal] Lee, J. / 2020 / BioBERT : a pre-trained biomedical language representation model for biomedical text mining / Bioinformatics 36(4) : 1234~1240

[other] Lee, S / 2020 / KR-BERT: A Small-Scale Korean-Specific Language Model / CoRR, arXiv:2008.03979

[journal] Locke, S. / 2021 / Natural language processing in medicine : a review / Trends in Anaesthesia and Critical Care 38 : 4~9

[journal] McCarthy, J. / 2006 / A proposal for the dartmouth summer research project on artificial intelligence / AI magazine 27(4) : 12~12

[confproc] Mikolov, T. / 2013 / Efficient estimation of word representations in vector space / 1st International Conference on Learning Representations, ICLR 2013, Workshop Track Proceedings

[journal] Nadkarni, P. M. / 2011 / Natural language processing : an introduction / Journal of the American Medical Informatics Association 18(5) : 544~551

[confproc] Pennington, J. / 2014 / Glove: Global vectors for word representation / Proceedings of the 2014 conference on empirical methods in natural language processing(EMNLP) : 1532~1543

[confproc] Peters, M. E. / 2018 / Deep contextualized word representations / Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 1 : 2227~2237

[report] Radford, A. / 2018 / Language Models are Unsupervised Multitask Learners / OpenAi

[journal] Ranjan, N. / 2016 / A Survey on Techniques in NLP / International Journal of Computer Applications 134(8) : 6~9

[journal] Rogers, A. / 2020 / A Primer in BERTology : What we know about how BERT works / Transactions of the Association for Computational Linguistics 8 : 842~866

[journal] Rosenblatt, F. / 1958 / The perceptron : a probabilistic model for information storage and organization in the brain / Psychological review 65(6) : 386~408

[confproc] Singh, P. / 2021 / A pilot study for BERT language modelling and morphological analysis for ancient and medieval Greek / Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2021) : 128~137

[confproc] Sutskever, I. / 2011 / Generating text with recurrent neural networks / Proceedings of the 28th International Conference on International Conference on Machine Learning (ICML'11) : 1017~1024

[confproc] Tang, D. / 2014 / Learning sentiment-specific word embedding for twitter sentiment classification / Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics 1 : 1555~1565

[confproc] Vaswani, A. / 2017 / Attention is all you need / Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17) : 6000~6010

[journal] Von Ahn, L. / 2008 / recaptcha : Human-based character recognition via web security measures / Science 321(5895) : 1465~1468

[other] Wang, H. / 2017 / On the origin of deep learning / arXiv preprint arXiv:1702.07800

[journal] Weikum, G. / 2002 / Foundations of statistical natural language processing / ACM SIGMOD Record 31(3) : 37~38

[journal] Weiss, K. / 2016 / A survey of transfer learning / Journal of Big data 3(1) : 1~40

[confproc] Wolf, T. / 2020 / Transformers: State-of-the-art natural language processing / Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations : 38~45

[other] Yang, Y. / 2020 / Finbert : A pretrained language model for financial communications / arXiv preprint arXiv:2006.08097

KJCKorea
Journal Central

Journal of Korean Society of Archives and Records Management 2023 KCI Impact Factor : 0.81

Considerations for Applying Korean Natural Language Processing Technology in Records Management

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of Korean Society of Archives and Records Management 2023 KCI Impact Factor : 0.81

Considerations for Applying Korean Natural Language Processing Technology in Records Management

ABSTRACT

KEYWORDS

Statistics

Tools

Issue List

Citation status

KCI Citation Counts (5)

REFERENCES (62) * References for papers published after 2023 are currently being built.

Search PDF

Citation

* References for papers published after 2023 are currently being built.