Heiko Holz
2026
Criterial Features in German: Towards Interpretable NLP in Readability Assessment
Denise Loefflad | Sofia Kathmann | Heiko Holz | Detmar Meurers
Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)
Denise Loefflad | Sofia Kathmann | Heiko Holz | Detmar Meurers
Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)
This paper presents an empirical evaluation of the German Grammar Profile (GGP), a CEFR-aligned resource of criterial features, and its corresponding extraction system PALME. We design a systematic test suite in which each feature extractor is evaluated on controlled positive and negative examples. The results show that PALME achieves high precision and recall across all CEFR levels, with over 90% of features achieving scores above 0.8. Qualitative analysis shows that lower performance primarily results from morphological ambiguity in noun and adjective case marking. To evaluate the usefulness of the criterial features of the GGP for CEFR-aligned readability assessment, we assess their predictive power using Explainable Boosting Machines on graded readers. The model achieves strong performance (precision: 0.75, recall: 0.73). Our qualitative analysis shows that features related to specific verb constructions follow patterns consistent with developmental stages predicted by Processability Theory. These findings underline the value and relevance of criterial features for modeling language development in readability assessment.
2018
COAST - Customizable Online Syllable Enhancement in Texts. A flexible framework for automatically enhancing reading materials
Heiko Holz | Zarah Weiss | Oliver Brehm | Detmar Meurers
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
Heiko Holz | Zarah Weiss | Oliver Brehm | Detmar Meurers
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
This paper presents COAST, a web-based application to easily and automatically enhance syllable structure, word stress, and spacing in texts, that was designed in close collaboration with learning therapists to ensure its practical relevance. Such syllable-enhanced texts are commonly used in learning therapy or private tuition to promote the recognition of syllables in order to improve reading and writing skills. In a state of the art solutions for automatic syllable enhancement, we put special emphasis on syllable stress and support specific marking of the primary syllable stress in words. Core features of our tool are i) a highly customizable text enhancement and template functionality, and ii) a novel crowd-sourcing mechanism that we employ to address the issue of data sparsity in language resources. We successfully tested COAST with real-life practitioners in a series of user tests validating the concept of our framework.