Diverse Linguistic Features for Assessing Reading Difficulty of Educational Filipino Texts
This work addresses the need for quality reading materials in Filipino education, but it is incremental as it applies existing methods to a specific language context.
The paper tackled the problem of automatically assessing the reading difficulty of educational Filipino texts by developing machine learning models using diverse linguistic features, achieving an accuracy of 62.7% with a Random Forest model and 66.1% with an optimal feature combination.
In order to ensure quality and effective learning, fluency, and comprehension, the proper identification of the difficulty levels of reading materials should be observed. In this paper, we describe the development of automatic machine learning-based readability assessment models for educational Filipino texts using the most diverse set of linguistic features for the language. Results show that using a Random Forest model obtained a high performance of 62.7% in terms of accuracy, and 66.1% when using the optimal combination of feature sets consisting of traditional and syllable pattern-based predictors.