PART-OF-SPEECH TAGGING ACCURACY IN ESL LEARNER CORPORA: AN ERROR-BASED COMPUTATIONAL STUDY
DOI:
https://doi.org/10.5281/zenodo.19389566Keywords:
Automated Error Analysis, ESL Learner Corpus, Interlanguage Theory, Part-of-Speech TaggingAbstract
This study investigates the accuracy of automated part-of-speech (POS) tagging in English as a Second Language (ESL) learner writing using an error-based computational approach. A corpus of fifteen learner essays, totaling approximately 2,870 tokens, was analyzed using the TagAnt POS tagging tool, and the automated outputs were meticulously compared with manually verified correct tags. The analysis focused on identifying common tagging errors, classifying them into categories, and examining patterns in relation to interlanguage development. Findings indicate that verb-related errors, particularly tense misclassifications and subject–verb agreement issues, are the most prevalent, whereas noun, adjective, and adverb errors occur less frequently. These systematic deviations reflect learners’ transitional interlanguage systems rather than random mistakes, offering insights into developmental stages of second language acquisition. The study achieved an overall tagging accuracy of 97.8%, demonstrating the general reliability of automated tools while revealing their limitations when processing non-native writing. By linking computational error analysis with Interlanguage Theory, the research provides theoretical contributions to understanding learner language development and practical guidance for educators, curriculum designers, and computational linguists. The results emphasize the importance of targeted pedagogical interventions, refinement of automated tagging systems, and the application of corpus-based computational methods to improve the assessment and support of ESL learners. This study bridges the gap between linguistic theory and computational practice, highlighting the role of automated analysis in enhancing both educational outcomes and the development of language technologies.
Downloads
References
Ali, M., Khan, M., & Alharbi, Y. (2024). A conditional random field based approach for high accuracy part of speech tagging using language independent features. PeerJ Computer Science.
Andersen, G. (2025). The importance of context in part of speech tagging: enhancing accuracy and understanding.
Anthony, L. (2023). AntConc 4.1: A learner and researcher-friendly corpus analysis toolkit. Waseda University.
Biber, D., Conrad, S., & Reppen, R. (1998). Corpus linguistics: Investigating language structure and use. Cambridge University Press.
Bitchener, J., & Ferris, D. (2012). Written corrective feedback in second language acquisition and writing. Routledge.
Brezina, V. (2018). Statistics in corpus linguistics: A practical guide. Cambridge University Press.
Corder, S. P. (1967). The significance of learners’ errors. International Review of Applied Linguistics in Language Teaching, 5(4), 161–170.
Ellis, R. (2008). The study of second language acquisition (2nd ed.). Oxford University Press.
Expert Systems with Applications. (2025). Comparison of deep learning and machine learning models for POS tagging, 288, 128026.
Flowerdew, J. (2015). Corpus-based perspectives on second language acquisition and writing. In J. Flowerdew & M. Brock (Eds.), Academic writing for graduate students (3rd ed., pp. 15–36). University of Michigan Press.
Gardazi, N. M., Daud, A., Malik, M. K., et al. (2025). BERT applications in natural language processing: a review. Artificial Intelligence Review, 58, 166.
Granger, S. (2002). A bird’s-eye view of learner corpus research. In S. Granger, J. Hung, & S. Petch-Tyson (Eds.), Computer learner corpora, second language acquisition and foreign language teaching (pp. 3–33). John Benjamins.
Heilman, M., Collins-Thompson, K., Callan, J., & Eskenazi, M. (2014). Part-of-speech tagging for learner English. Language Resources and Evaluation, 48(1), 1–24.
Ishaq, M., Asim, M., Ahadullah, et al. (2025). Development of a novel Urdu language part of speech tagging system for improved Urdu text classification. Social Network Analysis and Mining, 15, 58.
Jurafsky, D., & Martin, J. H. (2020). Speech and language processing (3rd ed., draft). Pearson.
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., & McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 55–60.
Odlin, T. (1989). Language transfer: Cross-linguistic influence in language learning. Cambridge University Press.
Raja, K. R., Chaurasia, P. K., & Midhunchakkaravarthy. (2025). Evaluating machine learning and deep learning techniques for POS tagging in morphologically rich languages. International Journal of Basic and Applied Sciences.
Reppen, R. (2010). Using corpora in the language classroom. Cambridge University Press.
Selinker, L. (1972). Interlanguage. International Review of Applied Linguistics in Language Teaching, 10(3), 209–231.
Tetreault, J., & Chodorow, M. (2008). The ups and downs of preposition error detection in ESL writing. Proceedings of the 22nd International Conference on Computational Linguistics (COLING 2008), 865–872.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 M. Ihsan Haris, Dr. Aftab Akram (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
