From Labels to Facets: Building a Taxonomically Enriched Turkish Learner Corpus

2026-01-30

AI Summary

This study addresses the limitations of existing learner corpora, which typically employ flat annotation schemes that hinder the disentanglement of multiple linguistic dimensions and impede fine-grained analysis of error origins. To overcome this, the authors propose a linguistically grounded multidimensional taxonomy and develop a semi-automatic annotation expansion framework that transforms flat labels into rich, multifaceted structures integrating linguistic features and metadata. As a first application of this approach, they construct a Turkish learner corpus conforming to the proposed taxonomy, accompanied by detailed annotation guidelines and supporting tools to enable cross-dimensional error pattern mining. Experimental results demonstrate that the method achieves 95.86% accuracy at the facet level, substantially enhancing both query capabilities and analytical depth of the corpus.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Multimodal Learning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

In terms of annotation structure, most learner corpora rely on holistic flat label inventories which, even when extensive, do not explicitly separate multiple linguistic dimensions. This makes linguistically deep annotation difficult and complicates fine-grained analyses aimed at understanding why and how learners produce specific errors. To address these limitations, this paper presents a semi-automated annotation methodology for learner corpora, built upon a recently proposed faceted taxonomy, and implemented through a novel annotation extension framework. The taxonomy provides a theoretically grounded, multi-dimensional categorization that captures the linguistic properties underlying each error instance, thereby enabling standardized, fine-grained, and interpretable enrichment beyond flat annotations. The annotation extension tool, implemented based on the proposed extension framework for Turkish, automatically extends existing flat annotations by inferring additional linguistic and metadata information as facets within the taxonomy to provide richer learner-specific context. It was systematically evaluated and yielded promising performance results, achieving a facet-level accuracy of 95.86%. The resulting taxonomically enriched corpus offers enhanced querying capabilities and supports detailed exploratory analyses across learner corpora, enabling researchers to investigate error patterns through complex linguistic and pedagogical dimensions. This work introduces the first collaboratively annotated and taxonomically enriched Turkish Learner Corpus, a manual annotation guideline with a refined tagset, and an annotation extender. As the first corpus designed in accordance with the recently introduced taxonomy, we expect our study to pave the way for subsequent enrichment efforts of existing error-annotated learner corpora.

Problem

• learner corpus
• annotation
• flat labels
• error analysis
• linguistic dimensions

Innovation

• faceted taxonomy
• learner corpus
• semi-automated annotation
• annotation extension
• error analysis