Diverse linguistic features for assessing reading difficulty of educational Filipino texts

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

International Conference on Computers in Education, Asia-Pacific Society for Computers in Education

Publication Date

2021

Abstract

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.

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Disciplines

Reading and Language

Keywords

Readability (Literary style); Computational linguistics--Philippines

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