Readability Assessment of Chinese Linguistic Texts Based on Dependent Syntactic Networks

[1] Basma, B., & Savage, R. (2023). Teacher professional development and student reading in middle and high school: a systematic review and meta-analysis:. Journal of Teacher Education, 74(3), 214-228. Search in Google Scholar

[2] Hudson, A. K. (2023). Upper elementary teachers’ knowledge of reading comprehension, classroom practice, and student’s performance in reading comprehension. Reading Research Quarterly, 58(3), 351-360. Search in Google Scholar

[3] JDJ Ainley. (2020). Does student grade contribute to the declining trend in programme for international student assessment reading and mathematics in australia?. Australian Journal of Education, 64. Search in Google Scholar

[4] B, Y. G. A., B, X. G. A., & B, D. L. A. (2020). Utterance-focusing multiway-matching network for dialogue-based multiple-choice machine reading comprehension. Neurocomputing. Search in Google Scholar

[5] Williams, M., Wood, E., Arslantas, F., & Macneil, S. (2021). Examining chemistry students’ perceptions toward multiple-choice assessment tools that vary in feedback and partial credit. Canadian Journal of Chemistry(12), 99. Search in Google Scholar

[6] Mohd, D. Z., & Gerry, K. (2021). The digital humanities and re-imagined language description: a linguistic model of malay with potential for other languages. Digital Scholarship in the Humanities(4), 4. Search in Google Scholar

[7] Peng, J., Wang, C., & Lu, X. (2018). Effect of the linguistic complexity of the input text on alignment, writing fluency, and writing accuracy in the continuation task. Language Teaching Research, 24(3), 136216881878334. Search in Google Scholar

[8] Jin, H., & Liu, H. (2017). How will text size influence the length of its linguistic constituents?. Poznan Studies in Contemporary Linguistics. Search in Google Scholar

[9] Zhou, Y., & Xue, Y. (2020). Acrank: a multi-evidence text-mining model for alliance discovery from news articles. Information Technology & People, ahead-of-print(ahead-of-print). Search in Google Scholar

[10] Spinde, T., Rudnitckaia, L., Mitrovi, J., Hamborg, F., & Donnay, K. (2021). Automated identification of bias inducing words in news articles using linguistic and context-oriented features. Information Processing & Management, 58(3), 102505. Search in Google Scholar

[11] Lu, C., Bu, Y., Dong, X., Wang, J., Ding, Y., & Larivière, Vincent, et al. (2019). Analyzing linguistic complexity and scientific impact. Journal of Informetrics, 13. Search in Google Scholar

[12] Wang, W., Xu, Y., Wu, Y. J., & Goh, M. (2022). Linguistic understandability, signal observability, funding opportunities, and crowdfunding campaigns. Information & management(2), 59. Search in Google Scholar

[13] Sachan, M., Dubey, A., Hovy, E. H., Mitchell, T. M., & Xing, E. P. (2019). Discourse in multimedia: a case study in extracting geometry knowledge from textbooks. Computational Linguistics, 45(8), 1-35. Search in Google Scholar

[14] Zhang, L., Yan, Q., & Zhang, L. (2020). A text analytics framework for understanding the relationships among host self-description, trust perception and purchase behavior on airbnb. Decision Support Systems, 133, 113288. Search in Google Scholar

[15] MZA Ariely. (2019). Analyzing the language of an adapted primary literature article: towards a disciplinary approach of science teaching using texts. Science & Education, 28. Search in Google Scholar

[16] Crossley, S. A., Skalicky, S., & Dascalu, M. (2019). Moving beyond classic readability formulas: new methods and new models. Journal of Research in Reading, 42. Search in Google Scholar

[17] Berendes, K., Vajjala, S., Meurers, D., Bryant, D., Wagner, W., & Chikina, M., et al. (2017). Reading demands in secondary school: does the linguistic complexity of textbooks increase with grade level and the academic orientation of the school track?. Journal of educational psychology, 110, págs. 518-543. Search in Google Scholar

[18] Chen, X., & Meurers, D. (2018). Word frequency and readability: predicting the text-level readability with a lexical-level attribute. Journal of Research in Reading(3). Search in Google Scholar

[19] Martinc, M., Pollak, S., & Robnik-Ikonja, M. (2021). Supervised and unsupervised neural approaches to text readability. Computational Linguistics(1). Search in Google Scholar

[20] Hou, R., Huang, C. R., Ahrens, K., & Lee, Y. M. S. (2019). Linguistic characteristics of chinese register based on the menzerath—altmann law and text clustering. Digital Scholarship in the Humanities. Search in Google Scholar