Developing a machine learning-based grade level classifier for Filipino children’s literature (original) (raw)

2019

Abstract

Reading is an essential part of children’s learning. Identifying the proper readability level of reading materials will ensure effective comprehension. We present our efforts to develop a baseline model for automatically identifying the readability of children’s and young adult’s books written in Filipino using machine learning algorithms. For this study, we processed 258 picture books published by Adarna House Inc. In contrast to old readability formulas relying on static attributes like number of words, sentences, syllables, etc., other textual features were explored. Count vectors, Term FrequencyInverse Document Frequency (TF-IDF), n-grams, and character-level n-grams were extracted to train models using three major machine learning algorithms–Multinomial Naïve-Bayes, Random Forest, and K-Nearest Neighbors. A combination of K-Nearest Neighbors and Random Forest via voting-based classification mechanism resulted with the best performing model with a high average training accuracy ...

Joseph Marvin Imperial hasn't uploaded this paper.

Let Joseph Marvin know you want this paper to be uploaded.

Ask for this paper to be uploaded.