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This paper introduces a layered taxonomy for annotating grammatical errors in Chinese learner writing, integrating computational error correction with pedagogical insights. The approach categorizes errors at both character and punctuation levels, as well as through a three-layer core label system that considers edit operations, linguistic domains, and parts of speech. Evaluation through coverage analysis and a consistency study with large language models reveals the effectiveness of the taxonomy while highlighting areas for further refinement in category boundaries.
A novel layered taxonomy reveals critical insights into the complexities of grammatical error annotation in Chinese learner writing, bridging computational and pedagogical perspectives.
Grammatical error annotation in Chinese learner writing requires labels that are both consistent and linguistically meaningful. This paper proposes a layered scheme linking computational Chinese grammatical error correction (CGEC) with pedagogical error analysis. The scheme first identifies character- and punctuation-level orthographic errors, labeling them by edit operation and subtype. Other errors receive a three-layer core label combining edit operation, linguistic domain, and part of speech, with optional Chinese-specific extensions for aspect, modality, comparison, argument structure, and complements. Drawing on CGEC resources, learner-error taxonomies, and Mandarin grammar, the taxonomy is evaluated through a coverage analysis of automatically extracted MuCGEC edits and a preliminary consistency study in which five large language models apply it to a sample. The results support the layered approach while identifying category boundaries requiring further refinement.
