Modeling the Readability of German Targeting Adults and Children: An empirically broad analysis and its cross-corpus validation

Zarah Weiß, Detmar Meurers


Abstract
We analyze two novel data sets of German educational media texts targeting adults and children. The analysis is based on 400 automatically extracted measures of linguistic complexity from a wide range of linguistic domains. We show that both data sets exhibit broad linguistic adaptation to the target audience, which generalizes across both data sets. Our most successful binary classification model for German readability robustly shows high accuracy between 89.4%–98.9% for both data sets. To our knowledge, this comprehensive German readability model is the first for which robust cross-corpus performance has been shown. The research also contributes resources for German readability assessment that are externally validated as successful for different target audiences: we compiled a new corpus of German news broadcast subtitles, the Tagesschau/Logo corpus, and crawled a GEO/GEOlino corpus substantially enlarging the data compiled by Hancke et al. 2012.
Anthology ID:
C18-1026
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
303–317
Language:
URL:
https://aclanthology.org/C18-1026
DOI:
Bibkey:
Cite (ACL):
Zarah Weiß and Detmar Meurers. 2018. Modeling the Readability of German Targeting Adults and Children: An empirically broad analysis and its cross-corpus validation. In Proceedings of the 27th International Conference on Computational Linguistics, pages 303–317, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Modeling the Readability of German Targeting Adults and Children: An empirically broad analysis and its cross-corpus validation (Weiß & Meurers, COLING 2018)
Copy Citation:
PDF:
https://aclanthology.org/C18-1026.pdf