Towards Advancing Creativity in Libraries Using Natural Language Processing for Data Curation: A Bibliometric Analysis

Abstract

Natural language processing has significantly become one of the most advancing topics in the field of artificial intelligence. Knowing its capabilities of using semantics to extract relevant information from large volumes of data, its applications for data curation are worth exploring. NLP has an increasingly signifying role in different fields; therefore this study aims to examine the research output of the NLP for data curation. For this purpose, a bibliometric study on NLP for data curation is presented and research publications are retrieved from Scopus Database. The network analysis is done using the VOSviewer software. The results have shown an increase in the publication from the year 2014-2021 with an average of 29-30 publications each year. The USA is found to be dominating the field among other countries. Among the most prolific authors,Ananiadou, Vijay-Shanker, and Hirschman lead the list, accounting for 7.63% of the total output. The five main thematic areas identified are natural language processing, human, information processing, information retrieval, and data mining. The bibliometric analysis of NLP for data curation, uncovering the present status, paves the way for future research opportunities in libraries.This study will allow us to explore and understand research developments systematically and optimize libraries by employing NLP models for data curation for effective transmission of data for the end users.