Current Trends in Named Entity Recognition from Automatic Speech Recognition: A Bibliometric Analysis Using Scopus Database

Thu Hien Nguyen; Tuan Linh Nguyen; Thanh Binh Nguyen

doi:10.46604/aiti.2024.13619

Authors

Thu Hien Nguyen Faculty of Mathematics, Thai Nguyen University of Education, Thai Nguyen, Vietnam
Tuan Linh Nguyen Faculty of Electronics Enginee, Thai Nguyen University of Technology, Thai Nguyen, Vietnam
Thanh Binh Nguyen Faculty of Physics, Thai Nguyen University of Education, Thai Nguyen, Vietnam

DOI:

https://doi.org/10.46604/aiti.2024.13619

Keywords:

named entity recognition, automatic speech recognition, bibliometrics, Scopus, potential research direction

Abstract

Named entity recognition (NER) is critical for language understanding and text mining systems, such as event extraction and automatic question-and-answer systems. However, NER from automatic speech recognition (ASR) outputs remains challenging due to errors and lack of textual cues. This study aims to provide a comprehensive bibliometric analysis of research on NER from ASR, focusing on publications indexed in the Scopus database before 2024 to understand the research field. Using Biblioshiny and VOSviewer tools, this research identifies the key trends, prominent authors, and international collaborations in the research network. The results show steady growth in this research area, while conference papers are the predominant source type. Additionally, the study highlights the increasing intervention of deep learning approaches to enhance NER accuracy, suggesting potential research directions to reduce error rates, and developing more robust NER algorithms. Finally, the findings underscore the importance of cross-disciplinary collaborations to document any current challenges.

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