CONTEXT-AWARE ERROR DETECTION IN SECOND LANGUAGE WRITING: A HYBRID LINGUISTIC–NEURAL APPROACH
Authors
Dr. Lukas Schneider ()Files
Key Information
Journal
Published in Vol. 1 No. 1 (2026) of Omega Journal of Linguistics and Language Studies.
OMEGA Journal of Linguistics and Language Studies is an international, peer-reviewed, open access journal dedicated to advancing research in linguistics, language, and communication. The journal provides a platform for scholars, educators, and researchers to publish high-quality work in both theoretical and applied aspects of language studies across diverse linguistic and cultural contexts.
Key Highlights:
- Peer-reviewed (Double-blind)
- Open Access Journal
- Monthly Publication
- International Scope
Frequency of Publication: Monthly
Languages: English, Polish and Russian
Publication Format: Electronic
ISSN (E): 3072-4678
About the JournalAbstract
Automated error detection in second language (L2) writing has advanced significantly with the development of neural language models. However, current systems often struggle with context-sensitive errors that require deeper linguistic interpretation. This paper proposes a Hybrid Context-Aware Error Detection Model (HCA-EDM) that integrates rule-based linguistic constraints with neural sequence modeling. The study evaluates the model conceptually using representative learner error types, demonstrating how hybridization improves detection accuracy for syntactic and semantic errors. The findings suggest that purely data-driven approaches are insufficient for capturing complex linguistic patterns, and that integrating linguistic knowledge enhances model interpretability and performance. The paper contributes to computational linguistics by offering a theoretically informed model for L2 error detection.
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