A Lightweight Regression Method to Infer Psycholinguistic Properties for Brazilian Portuguese
Table 5: Evaluating Psycholinguistic and Classic readability formulas for readability prediction.
['Features', 'Flesch', 'Honoré', 'Concreteness', 'Familiarity', 'AoA', 'Dale-Chall', 'Gunning Fox', 'Subjective Frequency', 'Psycholinguistics', 'MATTR', 'Brunét']
In order to evaluate the use of psycholinguistic properties to predict the readability level of BP informative texts from newspapers and magazines for early school years, we trained a classifier using a corpus of 1,413 texts which were classified as ease to read for 3rd to 6th graders. These texts were annotated by 2 linguists (0,914 weighted kappa) and the corpus distribution by grade levels is 183 texts for 3rd grade, 361 texts for 4th grade, 537 texts for 5th grade and 332 texts for 6th-grade year. We compared the use of our four psycholinguistic features and six traditional readability formulas: Flesch Reading-Ease adapted to BP Brunét, Honoré, Dale-Chall, Gunning Fox and Moving Average Type-Token Ratio (MATTR). We used the mean and the average of our psycholinguistic properties as features to train SVM classifiers with RBF kernel for each psycholinguistic information and a single-view classifier for all four properties. All results presented here were obtained by a 10-fold cross-validation process. The single-view classifier of psycholinguistic features overcame all traditional formulas but MATTR and Brunét Index for grade-level classification in F1-measure. Both MATTR and Brunét Index measure the lexical diversity of a text and are independent of text length. Their high performance in this evaluation suggests that lexical diversity is a strong proxy to distinguish grade levels in primary school years.