Abstract
Readability assessment plays an important role in selecting proper reading materials for language learners, and is applicable for many NLP tasks such as text simplification and document summarization. In this study, we designed 100 factors to systematically evaluate the impact of four levels of linguistic features (shallow, POS, syntactic, discourse) on predicting text difficulty for L1 Chinese learners. We further selected 22 significant features with regression. Our experiment results show that the 100-feature model and the 22-feature model both achieve the same predictive accuracies as the BOW baseline for the majority of the text difficulty levels, and significantly better than baseline for the others. Using 18 out of the 22 features, we derived one of the first readability formulas for contemporary simplified Chinese language.
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Notes
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This 18-feature formula and another 22-feature formula not presented in this paper are pending patent application.
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Acknowledgements
This work was supported by National Social Science Fund (Grant No. 17BGL068). We thank Taipeng Li, Qiuxia Liu, Nankang Liang, Shuying Liu, Jiahao Wei, and workshop participants at Southwestern University of Finance and Economics for their helpful comments and support.
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Qiu, X., Deng, K., Qiu, L., Wang, X. (2018). Exploring the Impact of Linguistic Features for Chinese Readability Assessment. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_67
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DOI: https://doi.org/10.1007/978-3-319-73618-1_67
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