linguistic-features
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- PaperDOAJ — Language assessment1 Mar 2026
Multi-tier linguistic feature engineering for CEFR classification: a comprehensive analysis of deterministic and machine learning-based features
Tendai M. Chikake, Elena M. Bazanova, Anna V. Gorizontova
Analyzed 133 linguistic features for automated CEFR classification using a two-tier architecture combining deterministic and machine-learning features. The best model achieved 66.72% exact accuracy and 94.53% adjacent accuracy on 3,198 learner texts, with strongest signals from morphological complexity and lexical sophistication.
Original abstract
We analyzed 133 linguistic features for automated proficiency classification under the Common European Framework of Reference (CEFR) in a two-tier architecture: deterministic Tier 1 (lexical, morphological, and syntactic measures) and machine-learning-based Tier 2 (semantic coherence, topic structure, cohesion, and error-analysis signals). Experiments were conducted on a corpus of 3,205 learner texts from mixed sources, with triangulated validation against expert-verified Cambridge examination subsets. The materials were collected in 2022--2025 and included a substantial institutional corpus of over 3,000 essays and other writing texts produced by students of Moscow Institute of Physics and Technology (MIPT) studying English as a foreign language and regularly assessed by our AI-powered testing system ISTOK (Intelligent System for Testing General Language Competencies). Feature matrices were standardized after missing-value handling (fold-local median imputation for cross-validation and zero-fill for held-out reporting). In supervised evaluation, the best Tier 1+2 model reaches 66.72% exact accuracy (macro F1 = 0.69) and 94.53% adjacent accuracy (within one CEFR level) on a 3,198-sample CEFR-labeled benchmark split; an extended comparison including preliminary Tier 3 features achieves 67.50%. Unsupervised analyses show strong structure for extreme levels (A1 99.5% purity; C2 82.4% purity) and moderate alignment with professional Cambridge labels (Adjusted Rand Index = 0.303). We report block ablations and compact subset searches, with strongest signals from morphological complexity and lexical sophistication, and consistent incremental gains from error-based features. The results provide a validated, interpretable feature inventory and practical guidance for feature selection in automated language assessment systems.