Acceso usuarios
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.
