Earticle

초록

영어

The exponential increase in publications and the interconnected nature of sub-domains make traditional methods of information extraction and organization inadequate. This inefficiency can impede scientific progress and innovation. To address these challenges, this research leverages the ability of Bidirectional Encoder Representations from Transformers for keyword extraction (KeyBERT) and integrates with K-Means clustering to organize topics from large datasets effectively. Analyzing a dataset of 47,627 articles from SCOPUS in the domains of Reinforcement Learning and Computer Vision. An ablation study demonstrates the generalizability of the approach across these fields, with the optimal number of clusters determined to be three using the Elbow Method. The results demonstrate that KeyBERT is effective in extracting and organizing topics within these domains, with a particular focus on applications such as medical imaging, autonomous driving, and real-time detection systems. This methodology offers a scalable solution for organizing vast academic datasets, enabling researchers to extract meaningful insights efficiently and apply this approach to other domains.

목차

Abstract
1. Introduction
2. Literature Review
2.1 Keyword Extraction with BERT
2.2 Topic Clustering
3. Methodology
3.1 Data Collection and Preprocessing
3.2 Keyword Extraction and Topic Clustering
4. Result and Discussion
5. Conclusion
Acknowledgement
References

키워드

저자

• Yosua Setyawan Soekamto [ PhD Student at Department of Computer Engineering, Dongseo University, Busan, South Korea/Lecturer at Department of Information Systems, Universitas Ciputra Surabaya ]
• Leonard Christopher Limanjaya [ Master Student at Department of Computer Engineering, Dongseo University, Busan, South Korea ]
• Yoshua Kaleb Purwanto [ Master Student at Department of Computer Engineering, Dongseo University, Busan, South Korea ]
• Bongjun Choi [ Professor at Department of Software, Dongseo University, Busan, South Korea ]
• Seung-Keun Song [ Professor at Department of Visual Contents, Graduate School, Dongseo University, Busan, South Korea ]
• Dae-Ki Kang [ Professor at Department of Computer Engineering, Dongseo University, Busan, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380