A Bibliometric Analysis of Generative Linguistics: A Study Based on Web ofScience Data

Abstract

This study is based on a comprehensive bibliometric analysis of 1,908 publications containing the key concept of generative linguistics between 1975 and 2025. The aim of the study is to systematically reveal the temporal trends, thematic orientations, and publication patterns of generative linguistics literature through these publications indexed in the Web of Science database. Performance analysis, conceptual mapping, co-occurrence and co-citation networks, historiographic analysis, and thematic trend analyses were employed in the research. The findings show that generative linguistics gained momentum rapidly after 2010, especially from 2018 onwards, with international visibility reaching its highest level in the period 2021-2024. The field's institutional leaders include the University of Cambridge, Harvard University, the University of Edinburgh, and Stanford University. The US, China, and the UK stand out as the most productive countries. Conceptually, the literature has expanded from classical linguistics topics, such as syntax, grammar, and semantics, to new AI-focused themes, including artificial intelligence, generative AI, ChatGPT, and large language models. This intellectual line, which began with Chomsky's theoretical contributions, has taken a new direction with the emergence of deep learning and natural language processing studies since 2017. In general, generative linguistics is evolving into an interdisciplinary research field that integrates artificial intelligence while maintaining its deep theoretical foundations, and it is expected to exhibit strong growth potential in the coming years.

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