Recognizing software names in biomedical literature using machine learning

Evolution and emerging trends of named entity recognition: Bibliometric analysis from 2000 to 2023

Identifying valuable information within the extensive texts documented in natural language presents a significant challenge in various disciplines. Named Entity Recognition (NER), as one of the critical technologies in text data processing and mining, has become a current research hotspot. To accurately and objectively review the progress in NER, this paper employs bibliometric methods. It analyzes 1300 documents related to NER obtained from the Web of Science database using CiteSpace software. Firstly, statistical analysis is performed on the literature and journals that were obtained to explore the distribution characteristics of the literature. Secondly, the core authors in the field of NER, the development of the technology in different countries, and the leading institutions are explored by analyzing the number of publications and the cooperation network graph. Finally, explore the research frontiers, development tracks, research hotspots, and other information in this field from a scientific point of view, and further discuss the five research frontiers and seven research hotspots in depth. This paper explores the progress of NER research from both macro and micro perspectives. It aims to assist researchers in quickly grasping relevant information and offers constructive ideas and suggestions to promote the development of NER.