Knowledge mapping of global research on natural language processing, 1958–2023

Abstract

Natural language processing (NLP) research has been on the increase since the first study in the area was published in 1958. This article examines the document types, source types, source titles, affiliations, open access types, funders, countries of affiliation of researchers, volume and growth of literature (1958–2023) and growth of NLP literature (1958–2023), and visualises and maps all keywords on the area as indexed in Scopus during the period. Basic bibliometric indicators, including all keywords, were collected covering the period 1958 to 2023 on 13 May 2024. NLP research predominantly appears in conference proceedings and specialised sources in computer science, despite its interdisciplinary nature. Keyword analysis reveals diverse themes in NLP, including computational linguistics, semantics and human-computer interaction. The field’s multidisciplinary approach merges concepts from computational linguistics and computer science to develop systems capable of understanding and processing natural languages. Deep learning and machine learning algorithms are pivotal, alongside a focus on semantics and performance optimisation to extract insights and enhance efficiency. NLP research epitomises a collaborative effort to bridge the gap between human language and computational systems, driving innovation and progress in understanding and utilising language in diverse contexts.