A Bibliometric Study of Natural Language Processing Using Dimensions Database: Development, Research Trend, and Future Research Directions

Abstract

A division of artificial intelligence known as natural language processing is thought to be a rapidly developing subject. NLP methods enable computers to comprehend human words and translate unstructured data for use by systems. The Dimensions database, which contains papers on natural language processing from 2002 to 2021, is thoroughly examined bibliometrically in this paper. It is the first attempt to perform bibliometric analysis in the NLP area in Dimensions database papers. The information is examined using bibliometric growth indicators, such as growth rate, relative growth rate, doubling time, degree of collaboration, and collaboration index. In addition, the VOSviewer software is used to create bibliometric networks. The primary purposes of this research study include detecting the NLP development pattern, discovering the distribution of co-authorship, influential authors, productive geographical distribution, keyword analysis, highly cited publications, finding recent research hotspots, and determining future research directions based on the challenges that exist in each of the research trends.