A Review of The Application of Natural Language Processing in Human-Computer Interaction

Jindi Song

doi:10.54254/2755-2721/106/20241328

Research Article

Open access

Published on 26 November 2024

Download pdf

Song,J. (2024). A Review of The Application of Natural Language Processing in Human-Computer Interaction. Applied and Computational Engineering,106,111-117.

Export citation

A Review of The Application of Natural Language Processing in Human-Computer Interaction

Jindi Song *^,1,

¹ College of Foreign Languages and Literatures, Department of Foreign Languages, Taiyuan University of Technology, Taiyuan, China, 030024

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/106/20241328

Abstract

Over the past few decades, advancements in computing technologies have propelled research in Human-Computer Interaction (HCI). Recently, the rise of artificial intelligence, particularly deep learning and pre-trained models like BERT and GPT, has revolutionized Natural Language Processing (NLP), making HCI applications more intelligent and personalized. NLP has emerged as a critical technology in enhancing HCI, enabling more intuitive and efficient communication between users and machines. This review explores the various applications of NLP in HCI, highlighting its significant role in user interface design, chatbots, and virtual assistants. Specifically, the paper examines how NLP techniques such as intent recognition, sentiment analysis, and language generation contribute to the creation of more responsive and user-friendly interfaces through voice input, personalized experiences, and optimized feedback mechanisms. Furthermore, the challenges and limitations of implementing NLP in HCI are discussed, particularly concerning data privacy, model effectiveness, and the ethical implications of deploying NLP systems, with a focus on privacy and trustworthiness. Finally, the paper considers future research directions in this field, emphasizing the importance of interdisciplinary collaboration in overcoming current barriers and enhancing the applicability of NLP in real-world contexts, while urging developers to prioritize responsible AI practices in their designs.

Keywords

Natural Language Processing, Human computer interaction, User Interface, Chatbots, Human-centered AI.

View pdf

References

[1]. Ramírez, J. G. C. (2024).Natural Language Processing Advancements: Breaking Barriers in Human-Computer Interaction. https://doi.org/10.60087/jaigs.v3i1.63

[2]. Mishra, A. R., & Garg, P. (2024).Recent Advancements in Text Summarization with Natural Language Processing. https://doi.org/10.2174/9789815238488124020004

[3]. Saini, N., Neha, B. S. P., & Singh, M. (2024).Natural Language Processing: A Historical Overview, Current Developments, and Future Prospects. https://doi.org/10.2174/9789815238488124020013

[4]. Reyburn, H. T. (2022). Human–Computer Interaction.The International Encyclopedia of Health Communication. https://doi.org/10.1002/9781119678816.iehc0729

[5]. Vanita,-. (2024). An Extant of Natural Language Processing.International Journal For Multidisciplinary Research. https://doi.org/10.36948/ijfmr.2024.v06i02.18326

[6]. Yamamoto, M. (2023). Evolution of Natural Language Processing Technology: Not Just Language Processing Towards General Purpose AI.arXiv.Org. https://doi.org/10.48550/arxiv.2310.06228

[7]. Sharma, A., & Gupta, D. (2023).From Sci-Fi to Reality: The Evolution of Human-Computer Interaction with Artificial Intelligence. https://doi.org/10.1109/ICAAIC56838.2023.10141431

[8]. Antona, M., Margetis, G., Ntoa, S., & Degen, H. (2023). Special Issue on AI in HCI.International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2023.2177421

[9]. Kulkarni, R., Varade, Dr. P., & Yellalwar, Dr. R. G. (2023). NLP and Human-Computer Interaction: Enhancing User Experience through Language Technology.International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.22214/ijraset.2023.56219

[10]. Albtoush, E. al. A. (2023). ChatGPT: Revolutionizing User Interactions with Advanced Natural Language Processing.International Journal on Recent and Innovation Trends in Computing and Communication. https://doi.org/10.17762/ijritcc.v11i9.9541

[11]. Tang, L., & Shu, T. (2024).English Language Translation Machine System Based on Human-Computer Interaction. https://doi.org/10.1007/978-3-031-55848-1_21

[12]. Lin, H., Ni, L., Phuong, C., & Hong, J. C. (2024). Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways.Pharmacogenomics and Personalized Medicine. https://doi.org/10.2147/pgpm.s396971

[13]. Wo’zniak, S., Koptyra, B., Janz, A., Kazienko, P., & Koco’n, J. (2024). Personalized Large Language Models.arXiv.Org. https://doi.org/10.48550/arxiv.2402.09269

[14]. Merizig, A., Belouaar, H., Bakhouche, M. M., & Kazar, O. (2024). Empowering customer satisfaction chatbot using deep learning and sentiment analysis.Buletin Teknik Elektro Dan Informatika. https://doi.org/10.11591/eei.v13i3.6966

[15]. Radev, D. (2023).Natural Language Processing and Text Analytics: Techniques and Applications. https://doi.org/10.59646/csebookc2/004

[16]. Bobba, P. S., Sailer, A., Pruneski, J., Beck, S., Mozayan, A., Mozayan, S., Arango, J., Cohan, A., & Chheang, S. (2023). Natural language processing in radiology: Clinical applications and future directions.Clinical Imaging. https://doi.org/10.1016/j.clinimag.2023.02.014

[17]. DiLosa, K. L., Xu, E., Kwong, M., Humphries, M. D., & Mell, M. W. (2023). Use of Natural Language Processing to Improve Surveillance of Aortic Aneurysms in Women Deemed Ineligible by Current Guidelines.Journal of Vascular Surgery. https://doi.org/10.1016/j.jvs.2023.03.284

[18]. Duminuco, A., Yeung, J. A., Vaghela, R., Virdee, S., Woodley, C., Asirvatham, S., Curto‐García, N., Sriskandarajah, P., O’Sullivan, J., Lavallade, H. de, Radia, D., Kordasti, S., Palumbo, G. A., Harrison, C., & Harrington, P. (2024). Development of a natural language processing pipeline for assessment of cardiovascular risk in myeloproliferative neoplasms.HemaSphere. https://doi.org/10.1002/hem3.143

[19]. Jayanth, K. K., Mohan, G. B., Kumar, R. P., & Rithani, M. (2024).Intent Recognition Leveraging XLM-RoBERTa for Effective NLU. https://doi.org/10.1109/icaaic60222.2024.10575275

[20]. Gunaratna, K., Srinivasan, V., & Jin, H. (2023).Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond. https://doi.org/10.1145/3583780.3615277

[21]. Leng, X.-L., Miao, X.-A., & Liu, T. (2021). Using recurrent neural network structure with Enhanced Multi-Head Self-Attention for sentiment analysis.Multimedia Tools and Applications. https://doi.org/10.1007/S11042-020-10336-3

[22]. Jia, Y. (2020).A Deep Learning System for Sentiment Analysis of Service Calls. https://doi.org/10.18653/V1/2020.ECNLP-1.4

[23]. Kaur, A., & Singh, A. (2023). Conversational natural language processing for automated customer support services.Nucleation and Atmospheric Aerosols. https://doi.org/10.1063/5.0100875

[24]. Lam, K. (2023). ChatGPT for low- and middle-income countries: a Greek gift?The Lancet Regional Health - Western Pacific. https://doi.org/10.1016/j.lanwpc.2023.100906

[25]. Chin, H., Song, H., Baek, G. B., Shin, M., Jung, C., Cha, M., Choi, J., & Cha, C. (2023). The Potential of Chatbots for Emotional Support and Promoting Mental Well-Being in Different Cultures: Mixed Methods Study.Journal of Medical Internet Research. https://doi.org/10.2196/51712

[26]. Fernandes, D., Garg, S., Nikkel, M., & Güven, G. (2024). A GPT-Powered Assistant for Real-Time Interaction with Building Information Models.Buildings. https://doi.org/10.3390/buildings14082499

[27]. Biswas, N., Biswas, S., & Maity, S. (2024).Analysis of Chatbots. https://doi.org/10.4018/979-8-3693-1830-0.ch004

[28]. Maher, S., Bhable, S. G., Lahase, A. R., & Nimbhore, S. S. (2022). AI and Deep Learning-driven Chatbots: A Comprehensive Analysis and Application Trends.International Conference Intelligent Computing and Control Systems. https://doi.org/10.1109/ICICCS53718.2022.9788276

[29]. Alia, P. A., Sari, D. K., Azis, N., Sudarsono, B. G., & Sucipto, P. A. (2024). Implementation Artificial Intelligence with Natural Language Processing Method to Improve Performance of Digital Product Sales Service.Advance Sustainable Science, Engineering and Technology (ASSET). https://doi.org/10.26877/asset.v6i3.521

[30]. Shankar, G. B., Suresh, M., & Lakshmi, T. (2024). Application of Chatbots and Virtual Assistants in Ticket Booking System.Deleted Journal. https://doi.org/10.47392/irjaem.2024.0221

[31]. Abiagom, C. N., & Ijomah, T. I. (2024). Enhancing customer experience through AI-driven language processing in service interactions.Open Access Research Journal of Engineering and Technology. https://doi.org/10.53022/oarjet.2024.7.1.0027

[32]. Widyastuti, N., Ferdiana, R., & Nugroho, L. E. (2023).A Review Transformation Customer Service with Customer Relationship Management (CRM) Based Chatbots. https://doi.org/10.1109/iwaiip58158.2023.10462818

[33]. Bachmann, P., Bucher, E., Buhmann, A., Castello, I., Colleoni, E., Fieseler, C., Illia, L., Romenti, S., & Zyglidopoulos, S. C. (2022). AI Text Agents: Spring of Hope or Winter of Despair for Business and Society?Proceedings - Academy of Management. https://doi.org/10.5465/ambpp.2022.14153symposium

Cite this article

Song,J. (2024). A Review of The Application of Natural Language Processing in Human-Computer Interaction. Applied and Computational Engineering,106,111-117.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation

Conference website: https://2024.confmla.org/

ISBN：978-1-83558-707-2(Print) / 978-1-83558-708-9(Online)

Conference date: 21 November 2024

Editor：Mustafa ISTANBULLU

Series: Applied and Computational Engineering

Volume number: Vol.106

ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).