Researchers have taken great interest in the assessment of text readability. This study expands on this research by developing readability models that predict the processing effort involved during first language (L1) and second language (L2) text reading. Employing natural language processing tools, the study focused on assessing complex linguistic features of texts, and these features were used to explain the variance in processing effort, as evidenced by eye movement data for L1 or L2 readers of English that were extracted from an open eye-tracking corpus. Results indicated that regression models using the indices of complex linguistic features provided better performance in predicting processing effort for both L1 and L2 reading than the models using simple linguistic features (word and sentence length). Furthermore, many of the predictive variables were lexical features for both L1 and L2 reading, emphasizing the importance of decoding for fluent reading regardless of the language used.

研究人员对文本可读性的评估非常感兴趣。本研究通过开发可读性模型来扩展这项研究，该模型可预测第一语言 (L1) 和第二语言 (L2) 文本阅读过程中涉及的处理工作。该研究采用自然语言处理工具，重点评估文本的复杂语言特征，这些特征用于解释处理工作的差异，从睁开眼睛提取的 L1 或 L2 英语读者的眼动数据可以证明这一点- 跟踪语料库。结果表明，与使用简单语言特征（单词和句子长度）的模型相比，使用复杂语言特征指数的回归模型在预测 L1 和 L2 阅读的处理工作量方面提供了更好的性能。此外，