Journal of Information Technology Services (한국IT서비스학회지)

Korea Society of IT Services (한국IT서비스학회)
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A Study on Use Case Analysis and Adoption of NLP: Analysis Framework and Implications

NLP 활용 사례 분석 및 도입에 관한 연구: 분석 프레임워크와 시사점

  • 박현정 (고려대학교 Human-inspired AI 연구소) ;
  • 임희석 (고려대학교 정보대학 컴퓨터학과)
  • Received : 2022.02.04
  • Accepted : 2022.03.28
  • Published : 2022.04.30
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Abstract

With the recent application of deep learning to Natural Language Processing (NLP), the performance of NLP has improved significantly and NLP is emerging as a core competency of organizations. However, when encountering NLP use cases that are sporadically reported through various online and offline channels, it is often difficult to come up with a big picture of how to understand and interpret them or how to connect them to business. This study presents a framework for systematically analyzing NLP use cases, considering the characteristics of NLP techniques applicable to almost all industries and business functions, environmental changes in the era of the Fourth Industrial Revolution, and the effectiveness of adopting NLP reflecting all business functional areas. Through solving research questions based on the framework, the usefulness of it is validated. First, by accumulating NLP use cases and pivoting them around the business function dimension, we derive how NLP techniques are used in each business functional area. Next, by synthesizing related surveys and reports to the accumulated use cases, we draw implications for each business function and major NLP techniques. This work promotes the creation of innovative business scenarios and provides multilateral implications for the adoption of NLP by systematically viewing NLP techniques, industries, and business functional areas. The use case analysis framework proposed in this study presents a new perspective for research on new technology use cases. It also helps explore strategies that can dramatically improve organizational performance through a holistic approach that encompasses all business functional areas.

Keywords

Acknowledgement

이 논문 또는 저서는 2020년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2020S1A5B5A16083616). 본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터지원사업의 연구결과로 수행되었음 (IITP-2018-0-01405).

References

  1. 과학기술정보통신부, 정보통신산업진흥원, 클라우드 산업실태조사 결과보고서, 2020.
  2. 국가통계포털, "경제활동별 국내총생산(OECD)", 2021, https://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_2KAA906_OECD (Accessed on Nov. 15, 2021).
  3. 김동완, "빅데이터의 분야별 활용사례", 경영논총, 제34권, 2013, 39-52.
  4. 김영욱, "고객의 비즈니스를 이해하는 대화형 AI", 한국인터넷진흥원(KISA), 2021.
  5. 김재생, "빅데이터 분석 기술과 활용사례", 한국콘텐츠학회지, 제12권, 제1호, 2014, 14-20. https://doi.org/10.20924/CCTHBL.2014.12.1.014
  6. 박문수, 이동희, "4차 산업혁명 시대 주요국 제조업과 서비스업 연계성 현황과 시사점", 산업연구원 ISSUE PAPER, 2017.
  7. 박성은, "코로나19로 시급해진 신약 개발, AI가 유망 해결사", AI타임스, 2021년 10월 1일.
  8. 박아름, 이새봄, 송재민, "인공지능 기반 챗봇 기술의 산업 적용 연구", 한국컴퓨터정보학회 논문지, 제25권, 제7호, 2020, 17-25.
  9. 복경수, 유재수, "빅데이터 활성화 정책 및 응용 사례", 정보과학회지, 제32권, 제11호, 2014, 46-57.
  10. 삼정KPMG 경제연구원, "음성 AI 시장의 동향과 비즈니스 기회", Issue Monitor, 제126호, 2020년 4월.
  11. 성경식, "디지털화한 '뉴노멀' 시대에 인간성 교감 더하기", 전자신문, 2021년 1월 14일.
  12. 우미영, "코로나가 바꾼 고객경험의 뉴 노멀", Adobe Blog, 2021년 3월 2일.
  13. 이경주, 김은영, "플랫폼 서비스 혁신에 있어 인공지능(AI)의 역할과 효과에 관한 연구: 카카오 그룹의 인공지능 활용 사례 연구", 지식경영연구, 제21권, 제1호, 2020, 175-195. https://doi.org/10.15813/KMR.2020.21.1.010
  14. 임철수, "IoT 서비스 활용사례 분석 및 산업 활성화 이슈", 한국차세대컴퓨팅학회 논문지, 제11권, 제6호, 2015, 41-50.
  15. 임희석, 고려대학교 자연어처리연구실, 자연어 처리 바이블, 휴먼 싸이언스, 2019.
  16. 한국데이터베이스진흥원, 데이터 분석 전문가 가이드, 한국데이터산업진흥원, 2020.
  17. 한국정보화진흥원, "인공지능 기반 챗봇 서비스의 국내외 동향분석 및 발전 전망", Trend & Future, 2018-2호, 2018.
  18. Agarwal, A., S. Maiya, and S. Aggarwal, "Evaluating Empathetic Chatbots in Customer Service Settings", 2021, arXiv:2101.01334 [cs.CL].
  19. Alibaba Clouder, "Natural Language Intelligence: Building a Language Bridge for Business", Alibaba Cloud Blog, 22 July 2020.
  20. Brereton, P., B.A. Kitchenham, D. Budgen, and Z. Li, "Using a protocol template for case study planning", 12th International Conference on Evaluation and Assessment in Software Engineering (EASE), Vol.8, 2008, 41-48.
  21. Brown, T.B., B. Mann, N. Ryder et al., "Language Models are Few-Shot Learners", 2020, arXiv:2005.14165 [cs.CL].
  22. Chew, P. A., "Natural Language Processing Meets Accounting, Increases Accuracy, and Reduces Costs", Report GCG003, Galisteo Consulting Group, 2016.
  23. CIO Online, "FutureEdge 50 Awards 2021", 2021, https://app.qwoted.com/opportunities/award-futureedge-50-awards-2021 (Accessed on Nov. 7, 2021).
  24. Curtis, S., W. Gesler, G. Smith, and S. Washburn, "Approaches to sampling and case selection in qualitative research: Examples in the geography of health", Social Science & medicine, Vol.50, No.7-8, 2000, 1001-1014. https://doi.org/10.1016/S0277-9536(99)00350-0
  25. Davenport, T.H., "From analytics to artificial intelligence", Journal of Business Analytics, Vol.1, No.2, 2018, 73-80. https://doi.org/10.1080/2573234x.2018.1543535
  26. Deloitte, Deloitte Insights Magazine, No.17, 2021.
  27. Devlin, J., M. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", 2018, arXiv:1810.04805 [cs.CL].
  28. FinText, 8 Case Studies in Banking and Investment Management, 2020.
  29. Fisher, I.E., M.R. Garnsey, and M.E. Hughes, "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research", International Journal of Intelligent Systems in Accounting and Finance Management, Vol.23, No.3, 2016, 157-214.
  30. Friedman, C., "A broad-coverage natural language processing system", Proceedings of AMIA Symposium, 2000, 270-274.
  31. Glaz, A.L., Y. Haralambous, D. Kim-Dufor, P. Lenca, R. Billot, T.C. Ryan, J. Marsh, J. DeVylder, M. Walter, S. Berrouiguet, and C. Lemey, "Machine Learning and Natural Language Processing in Mental Health: Systematic Review", Journal of Medical Internet Research, Vol.23, No.5, 2021, e15708. https://doi.org/10.2196/15708
  32. Kang, Y., Z. Cai, C. Tan, Q. Huang, and H. Liu, "Natural language processing (NLP) in management research: A literature review", Journal of Management Analytics, Vol.7, No.2, 2020, 139-172. https://doi.org/10.1080/23270012.2020.1756939
  33. Laudon, K.C. and J.P. Laudon, Management Information Systems: Managing the Digital Firm, Pearson, 2018.
  34. Lever, R., "Robo-journalism gains traction in shifting media landscape", AFP News, 10 March 2019.
  35. Litman, D., "Natural Language Processing for Enhancing Teaching and Learning", Proceedings of the AAAI Conference on Artificial Intelligence, Vol.30, No.1, 2016.
  36. Liu, B. and S. Mazumder, "Lifelong and Continual Learning Dialogue Systems: Learning during Conversation", Proceedings of the AAAI Conference on Artificial Intelligence, Vol.35, No.17, 2021, 15058-15063.
  37. Locke, S., A. Bashall, S. Al-Adely, J. Moore, A. Wilson, and G. B. Kitchen, "Natural language processing in medicine: A review", Trends in Anaesthesia and Critical Care, Vol.38, 2021, 4-9. https://doi.org/10.1016/j.tacc.2021.02.007
  38. Lorica, B. and P. Nathan, "2021 NLP Survey", Gradient Flow, 2021.
  39. Manning, C.D., C.D. Manning, and H. Schutze, "Foundations of Statistical Natural Language Processing", MIT Press, 1999.
  40. Marketsandmarkets, Natural Language Processing Market - Global Forecast to 2026, 2021.
  41. Mordor Intelligence, Natural Language Processing (NLP) Market - Growth, Trends, COVID-19 Impact and Forecasts (2021-2026), 2020.
  42. Mya, "Customer Stories", 2021, https://www.mya.com/customer-stories/ (Accessed on Nov. 28, 2021).
  43. Osborne, J., M. Wyatt, A. Westfall, J. Willig, S. Bethard, and G. Gordon, "Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning", Journal of the American Medical Informatics Association, 2016, 1077-1084. https://doi.org/10.1093/jamia/ocw006
  44. Ozan, S., "Case studies on using natural language processing techniques in customer relationship management software", Journal of Intelligent Information Systems, Vol.56, 2021, 233-253. https://doi.org/10.1007/s10844-020-00619-4
  45. Pamungkas, E.W., "Emotionally-Aware Chatbots: A Survey", 2019, arXiv:1906.09774 [cs.CL].
  46. Parr, C.S., D.G. Lemay, C.L. Owen, M.J. Wood-ward-Greene, and J. Sun, "Multimodal AI to Improve Agriculture", IT Professional, Vol.23, No.3, 2021, 53-57. https://doi.org/10.1109/MITP.2020.2986122
  47. Quarteroni, S., "Natural Language Processing for Industry", Informatik Spektrum, Vol.41, 2018, 105-112. https://doi.org/10.1007/s00287-018-1094-1
  48. Sengupta, R., "How Natural Language Processing can Revolutionize Human Resources", 2021, https://www.aihr.com/blog/natural-language-processing-revolutionize-human-resources/ (Accessed on Nov. 17, 2021).
  49. Smith, D.P., O. Oechsle, M.J. Rawling, E. Savory, A.M.B. Lacoste, and P.J. Richardson, "Expert-Augmented Computational Drug Repurposing Identified Baricitinib as a Treatment for COVID-19", Frontiers in Pharmacology, Vol.12, 2021, 709856. https://doi.org/10.3389/fphar.2021.709856
  50. Stevie Awards, "Verizon - Best Technical Support Solution", 2020, https://stevieawards.com/aba/verizon-best-technical-support-solution (Accessed on Dec. 10, 2021).
  51. Wang, J., H. Deng, B. Liu, A. Hu, J. Liang, L. Fan, X. Zheng, T. Wang, and J. Lei, "Systematic Evaluation of Research Progress on Natural Language Processing in Medicine Over the Past 20 Years: Bibliometric Study on PubMed", Journal of Medical Internet Research, Vol.22, No.1, 2020, e16816. https://doi.org/10.2196/16816
  52. Yin, R.K., Case Study Research: Design and Method, Newbury Park, CA: SAGE Publication, 2003.
  53. Zhang, X., X. Ming, Z. Liu, D. Yin, Z. Chen, and Y. Chang, "A Reference Framework and Overall Planning of Industrial Artificial Intelligence (I-AI) for New Application Scenarios", The International Journal of Advanced Manufacturing Technology, Vol.101, 2019, 2367-2389. https://doi.org/10.1007/s00170-018-3106-3