Üretici Dilbiliminin Bibliyometrik Analizi: Web of Science Verilerine Dayalı Bir Çalışma (1975-2025)

Öz

Bu araştırma, 1975-2025 yılları arasında üretici dilbilimi anahtar kavramını içeren 1.908 yayına dayalı kapsamlı bir bibliyometrik analiz üzerine kuruludur. Araştırmanın amacı, Web of Science veritabanında dizinlenen bu yayınlar üzerinden, üretici dilbilimi literatürünün zamansal eğilimlerini, tematik yönelimlerini ve yayın örüntülerini sistematik biçimde ortaya koymaktır. Araştırmada performans analizi, kavramsal haritalama, eşzamanlılık ve eşalıntı ağları, tarih yazımı ve tematik eğilim analizleri uygulanmıştır. Bulgular, üretici dilbiliminin 2010'dan sonra, özellikle 2018'den itibaren hızla ivme kazandığını ve uluslararası görünürlüğünün 2021-2024 döneminde en yüksek seviyesine ulaştığını göstermektedir. Bu alandaki kurumsal liderler arasında Cambridge Üniversitesi, Harvard Üniversitesi, Edinburgh Üniversitesi ve Stanford Üniversitesi bulunmaktadır. ABD, Çin ve İngiltere en üretken ülkeler olarak öne çıkmaktadır. Kavramsal olarak literatür, sözdizimi, gramer ve anlambilim gibi klasik dilbilim konularından, yapay zekâ, üretken yapay zekâ, ChatGPT ve büyük dil modelleri gibi yeni yapay zekâ odaklı temalara doğru genişlemiştir. Chomsky’nin teorik katkılarıyla başlayan bu entelektüel çizgi, 2017'den bu yana derin öğrenme ve doğal dil işleme çalışmalarının ortaya çıkmasıyla yeni bir yön almıştır. Genel olarak üretici dilbilim, derin teorik temellerini korurken yapay zekâyı bütünleştiren disiplinler arası bir araştırma alanı olarak gelişmektedir ve önümüzdeki yıllarda güçlü bir büyüme potansiyeli sergilemesi beklenmektedir.

Anahtar Kelimeler

• Üretici dilbilimi
• Web of Science
• bibliyometrik haritalama

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.

Keywords

• Generative linguistics
• Web of Science
• bibliometric mapping

Kaynakça

1. Al Hakim, R. N. A. (2022). Exploring trends and gaps in teaching linguistics research among undergraduate EFL students: A bibliometric analysis. REiLA: Journal of Research and Innovation in Language, 4(3), 352-361. https://doi.org/10.31849/reila.v4i3.9512
2. Andor, J. (2019). András Kertész: The historiography of generative linguistics. Folia Linguistica, 53(2), 567-573. https://doi.org/10.1515/flin-2019-2022
3. Antović, M. (2007). Half a Century of Generative Linguistics: What has the paradigm given to social science?. Facta Unıversıtatıs-Linguistics and Literature, 5(1), 31-46. http://facta.junis.ni.ac.rs/lal/lal2007/lal2007-04.pdf
4. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
5. Barrot, J. S., Acomular, D. R., Alamodin, E. A., & Argonza, R. C. R. (2020). Scientific mapping of English language teaching research in the Philippines: A bibliometric review of doctoral and master’s theses (2010–2018). RELC Journal. https://doi.org/10.1177/0033688220936764
6. Burton-Roberts, N., & Poole, G. (2006). ‘Virtual conceptual necessity’, feature-dissociation and the Saussurian legacy in generative grammar. Journal of Linguistics, 42(3), 575-–628. https://doi.org/10.1017/S0022226706004208
7. Chaudhary, A., & Singh, S. (2024). Noam Chomsky’s contribution to linguistics. International Journal for Multidisciplinary Research, 6(3). https://doi.org/10.36948/ijfmr.2024.v06i03.23721
8. Chen, X., Hao, J., Chen, J., Hua, S., & Hao, T. (2018). A bibliometric analysis of the research status of technology-enhanced language learning. In T. Hao, W. Chen, H. Xie, W. Nadee, & R. Lau (Eds.), Emerging technologies for education (SETE 2018). Lecture notes in computer science (Vol. 11284, pp. 219–229). Springer. https://doi.org/10.1007/978-3-030-03580-8_18

9. CheshmehSohrabi, M., & Mashhadi, A. (2023). Using data mining, text mining, and bibliometric techniques to analyze research trends and gaps in the field of language and linguistics. Journal of Psycholinguistic Research, 52(2), 607-630. https://doi.org/10.1007/s10936-022-09911-6
10. Chomsky, N. (1965). Aspects of the theory of syntax. MIT Press.
11. Chomsky, N. (1995). The minimalist program. MIT Press.
12. Corbalán, M. (2021). From generative linguistics to categorial grammars: Overt Subjects in Control Infinitives. The Bulletin of Symbolic Logic, 27(2), 215-215. https://doi.org/10.1017/bsl.2021.32
13. Croft, W. (2005). Word classes, parts of speech, and syntactic argumentation. Linguistic Typology, 9(3), 431-441.
14. Dąbrowska, E. (2015). What exactly is Universal Grammar, and has anyone seen it?. Frontiers in psychology, 6, 852.
15. Dékány, É. (2019). Foundations of generative linguistics. Acta Linguistica Academica. An International Journal of Linguistics, 66(3), 309-334. ttps://doi.org/10.1556/2062.2019.66.3.1
16. Donthu, N., Kumar, S., & Pattnaik, D. (2020). Forty-five years of Journal of Business Research: A bibliometric analysis. Journal of Business Research, 109, 1–14. https://doi.org/10.1016/j.jbusres.2019.10.039
17. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
18. Doyumğaç, İ. (2022). The effect of activities based on generative-transformational linguistics on the correct sentence writing skills of learners of Turkish as a foreign language. Atatürk University, Institute of Educational Sciences.
19. Doyumğaç, İ., & Tezcan, A. (2026). Mapping the literature of generative grammar through bibliometric analysis [[Manuscript in progress in progress]. Journal of History School, 0(0), 00-00. Dupré, G. (2021). What would it mean for natural language to be the language of thought? Linguistics and Philosophy, 44, 773-812. https://doi.org/10.1007/s10988-020-09304-9
20. Ersöz, B., Sağiroğlu, Ş., & Bülbül, H. İ. (2024, October). Generative artificial intelligence opportunities and threats: Bibliometric analysis. In 2024, the 6th International Conference on Computer Science and Engineering (UBMK). IEEE. https://doi.org/10.1109/ubmk63289.2024.10773519
21. Featherston, S. (2007). Data in generative grammar: The stick and the carrot. The Linguistic Review, 33(3), 269-318. https://doi.org/10.1515/TL.2007.020
22. Gentner, T. Q., Fenn, K. M., Margoliash, D., & Nusbaum, H. C. (2006). Recursive syntactic pattern learning by songbirds. Nature 440, 1204-1207. 10.1038/nature04675
23. Gong, Y., Lyu, B., & Gao, X. (2018). Research on teaching Chinese as a second or foreign language in and outside Mainland China: A bibliometric analysis. The Asia-Pacific Education Researcher, 27(4), 277–289. https://doi.org/10.1007/s40299-018-0385-2
24. Haegeman, L. (1995). The syntax of negation (Vol. 75). Cambridge University Press.
25. Halle, M. (1983). On distinctive features and their articulatory implementation. Natural Language & Linguistic Theory, 1(1), 91–105. https://www.jstor.org/stable/4047515
26. Halle, M. (1983). On distinctive features and their articulatory implementation. Natural Language & Linguistic Theory, 91-105.
27. Harris, R. A. (2010). Chomsky’s other revolution. University of Waterloo.
28. Jackendoff, R. (2003a). The structure of language: why it matters to education. In Talk for conference on Learning and the Brain, Linguistic Society of America, Cambridge, November (pp. 5-8).
29. Jackendoff, R. (2003b). Précis of foundations of language: Brain, meaning, grammar, evolution. Behavioral and brain sciences, 26(6), 651-707. https://doi.org/10.1017/S0140525X03000153
30. Johnson, K. (2014). Methodology in generative linguistics: scratching below the surface. Language Sciences, 44, 47-59. https://doi.org/10.1016/j.langsci.2014.02.001
31. Kertész, A. (2010a). From ‘scientific revolution ' to ‘unscientific revolution’: an analysis of approaches to the history of generative linguistics. Language Sciences, 32(5), 507-527. https://doi.org/10.1016/j.langsci.2010.02.002
32. Kertész, A. (2010b). Two notions of “research program” and the historiography of generative linguistics. Historiographia Linguistica, 37(1), 165-191. https://doi.org/10.1075/hl.37.1/2.07ker
33. Kertész, A. (2019). Fallacies in the historiography of generative linguistics. Foundations of Science, 24(4), 775-801. https://doi.org/10.1007/s10699-019-09612-9
34. Kodner, J., Payne, S., & Heinz, J. (2023). Why linguistics will thrive in the 21st century: A reply to Piantadosi (2023). arXiv preprint arXiv:2308.03228.
35. Lei, L., & Liao, S. (2017). Publications in linguistics journals from Mainland China, Hong Kong, Taiwan, and Macau (2003–2012): A bibliometric analysis. Journal of Quantitative Linguistics, 24(1), 54–64. https://doi.org/10.1080/09296174.2016.1260274
36. Lei, L., & Liu, D. (2019). Research trends in applied linguistics from 2005 to 2016: A bibliometric analysis and its implications. Applied Linguistics, 40(3), 540–561. https://doi.org/10.1093/applin/amy003
37. Lewis, R. L., & Phillips, C. (2015). Aligning theories of grammar with theories of performance: A case study in morphosyntax. Journal of Psycholinguistic Research, 44(1), 27–46. https://doi.org/10.1007/s10936-014-9329-z
38. Liao, S., & Lei, L. (2017). What we talk about when we talk about corpus: A bibliometric analysis of corpus-related research in linguistics (2000–2015). Glottometrics, 38, 1–20.
39. Liu, J., Wang, C., Liu, Z., Gao, M., Xu, Y., Chen, J., & Cheng, Y. (2024). A bibliometric analysis of generative AI in education: Current status and development. Asia Pacific Journal of Education, 44(1), 156–175. https://doi.org/10.1080/02188791.2024.2305170
40. Ludlow, P. (2011). The philosophy of generative linguistics. OUP Oxford.
41. Maier, E. (2015). Reference, binding, and presupposition: Three perspectives on the semantics of proper names. Erkenntnis, 80, 313-333. https://doi.org/10.1007/s10670-014-9702-1
42. Mancini, V. (2023). Mid-level generalizations of generative linguistics: Experimental robustness, cognitive underpinnings, and the interdisciplinarity paradox. Frontiers in Psychology, 14, Article 1172571. https://doi.org/10.3389/fpsyg.2023.1172571
43. Marantz, A. (2005). Generative linguistics within the cognitive neuroscience of language. The Linguistic Review, 22(2-4), 429-445. https://doi.org/10.1515/tlir.2005.22.2-4.429
44. Marino, C., & Gervain, J. (2019). The Impact of Generative Linguistics on Psychology: Language Acquisition, a Paradigm Example. Acta Linguistica Academica, 66(3), 371-396. https://doi.org/10.1556/2062.2019.66.3.4
45. Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman.
46. Martorell, J. (2018). Merging generative linguistics and psycholinguistics. Frontiers in Psychology, 9, 251. https://doi.org/10.3389/fpsyg.2018.00251
47. Mohsen, M. A. (2016). Contributions of Saudi institutions in applied linguistics journals indexed in SSCI: Perspectives from academics and journals’ editors. International Journal of Applied Linguistics and English Literature, 5(4), 102–109. https://doi.org/10.7575/aiac.ijalel.v.5n.4p.102
48. Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. El Profesional de la Información, 29(1), e290103. https://doi.org/10.3145/epi.2020.ene.03
49. Napoletano, T. (2017). Why truth-conditional semantics in generative linguistics is still the better Bet. Erkenn, 82(3), 673-692. https://doi.org/10.1007/s10670-016-9838-2
50. Nefdt, R. M. (2021). Structural realism and generative linguistics. Synthese, 1-27. https://doi.org/10.1007/s11229-020-02952-1
51. Neto, J. F., da Silva, A. M., & Kenedy, E. (2015). Some reflections about generative linguistics, its evolution, its achievements, and methods. Gragoatá, 20 (38), 111-141. https://doi.org/10.22409/gragoata.v20i38.33303
52. Nur’aeni, R., & Zalsahra, R. (2024). Bibliometric analysis of artificial intelligence. World Scientific and Innovative Science, 2(1). https://doi.org/10.58812/wsis.v2i01.563
53. Pater, J. (2009). Weighted constraints in generative linguistics. Cognitive Science, 33(6), 999-1035. https://doi.org/10.1111/j.1551-6709.2009.01047.x
54. Pater, J. (2019). Generative linguistics and neural networks at 60: Foundation, friction, and fusion. Language, 95, e41 - e74. https://muse.jhu.edu/article/719231
55. Pereplyotchik, D. (2020). Generative linguistics meets normative inferentialism: Part 1. Croatian Journal of Philosophy, 20(3), 311-352. https://hrcak.srce.hr/253667
56. Phillips, C., & Wagers, M. (2007). Relating structure and time in linguistics and psycholinguistics. In G. Gaskell (Ed.), The Oxford Handbook of Psycholinguistics (pp. 739-756). Oxford University Press.
57. Poeppel, D., & Embick, D. (2005). The relation between linguistics and neuroscience. In A. Cutler (Ed.), Twenty-first century psycholinguistics: Four cornerstones (pp. 103-120). Lawrence Erlbaum Associates.
58. Portelance, E., & Jasbi, M. (2024). On the goals of linguistic theory: Revisiting Chomskyan theories in the era of AI. arXiv. https://doi.org/10.48550/arxiv.2411.10533
59. Rechtin, L. K. (1976). Generative linguistic theory: some phılosophical considerations (Thesis No. 7711220) [Doctoral thesis, University of Cincinnati- United States-Ohio]. ProQuest Dissertations & Theses Global.
60. Shormani, M. Q. (2024). Generative linguistics' contribution to artificial intelligence: Where does this contribution lie? arXiv. https://doi.org/10.48550/arxiv.2410.20221
61. Shormani, M. Q. (2025). What fifty-one years of linguistics and artificial intelligence research tell us about their correlation: A scientometric analysis. Artificial Intelligence Review, 58, 379. https://doi.org/10.1007/s10462-025-11332-5
62. Silva, T. G. M., & Pilati, E. N. S. (2017). Generative linguistics and grammar in primary education (Linguística gerativa e gramática na educação básica). 18(41), 46-64.10.5935/1981-4755.20170024
63. Sprouse, J., & Almeida, D. (2013a). The role of experimental syntax in an integrated cognitive science of language. The Cambridge handbook of biolinguistics, 181(1), 202.
64. Sprouse, J., Schütze, C. T., & Almeida, D. (2013b). A comparison of informal and formal acceptability judgments using a random sample from Linguistic Inquiry 2001-2010. Lingua, 134, 219-248. https://doi.org/10.1016/j.lingua.2013.07.002
65. Terzian, G., & Corbalán, M. I. (2021). Simplicity of what? A case study from generative linguistics. Synthese, 198, 1-26. https://doi.org/10.1007/s11229-020-02649-5
66. Uzunboylu, H., & Genç, Z. (2017). Analysis of documents published in Scopus database on foreign language learning through mobile learning: A content analysis. Profile Issues in Teachers’ Professional Development, 19(Suppl. 1), 99-107.
67. Vajjala, S. (2024, May). Expositions: Generative artificial intelligence and applied linguistics. JALT Journal, 46(1), 55. https://doi.org/10.37546/JALTJJ46.1-3
68. van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
69. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (Vol. 30). https://papers.nips.cc/paper/7181-attention-is-all-you-need
70. Wagers, M. (2022). Memory for linguistic features and the focus of attention. Frontiers in Psychology, 13, Article 980290. https://doi.org/10.3389/fpsyg.2022.980290
71. Walmsley, J. B. (2006). Chomsky, Noam (b. 1928). In L. Brown (Ed.), Encyclopedia of language & linguistics (2nd ed., pp. 335-338). Elsevier. https://doi.org/10.1016/B0-08-044854-2/01246-3
72. Wasow, T. (2005). Postverbal behavior. In K. Brown (Ed.), Encyclopedia of Language & Linguistics (2nd ed.). Elsevier.
73. Watumull, J., Hauser, M. D., Roberts, I. G., & Hornstein, N. (2014). On recursion. Frontiers in Psychology, 4, Article 1017. https://doi.org/10.3389/fpsyg.2013.01017
74. Zawacki-Richter, O., & Latchem, C. (2018). Exploring four decades of research in Computers & Education. Computers & Education, 122, 136-152. https://doi.org/10.1016/j.compedu.2018.04.001
75. Zhang, R., Cheng, G., & Chen, X. (2020). Game-based self-regulated language learning: Theoretical analysis and bibliometrics. PLoS ONE, 15(12), e0243827. https://doi.org/10.1371/journal.pone.0243827