A Corpus-Based Comparative Study of Derivational Morphemes Across ENL, ESL, EFL Learners Through ICNALE



  • Muhammad Farukh Arslan PhD Scholar, Department of Applied Linguistics, Government College University, Faisalabad, Pakistan.
  • Prof. Dr. Muhammad Asim Mahmood Dean, Faculty of Arts and Social Sciences, Government College University, Faisalabad, Pakistan.
  • Attia Rasool PhD Scholar, Department of Applied Linguistics, Government College University, Faisalabad, Pakistan.


Morphemes , Derivational, Inflectional morphemes, Morphemic pattern, ICNALE


This corpus-based comparative study was about morphemic derivational patterns in grammatical categories: adjective, noun and verbs in different varieties: English as native language (ENL), English as second language (ESL), and English as foreign language (EFL). This study was done on data collected from ICNALE in which learners’ data from three different varieties of English was compared. The data was tagged through CLAWS tagger and analyzed through AntConc software. In result of analysis, the frequency-based differences in the morphemic derivational patterns were observed after normalizing the data. Such differences across varieties in morphemic patterns were realized through the existence and absence of derivational morphemes. The results showed that the native speakers have higher ability of using a greater number of morphemic patterns than second and foreign language speakers of English. Due to their native like competence, they are more competent is the usage of morphemic derivational patterns. Those distinctive patterns should also be taken as pedagogical implication for second and foreign language learners of English. It can also be helpful for second and foreign language learners in achieving native like ability to use English language.


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How to Cite

Arslan, M. F., Mahmood, P. D. M. A., & Rasool, A. . . (2020). A Corpus-Based Comparative Study of Derivational Morphemes Across ENL, ESL, EFL Learners Through ICNALE. Linguistic Forum - A Journal of Linguistics, 2(4), 1–12. https://doi.org/10.53057/linfo/2020.2.4.1