Diverse Linguistic Features for Assessing Reading Difficulty of Educational Filipino Texts

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

Abstract

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.

Original languageEnglish
Title of host publication29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
EditorsMaria Mercedes T. Rodrigo, Sridhar Iyer, Antonija Mitrovic, Hercy N. H. Cheng, Dan Kohen-Vacs, Camillia Matuk, Agnieszka Palalas, Ramkumar Rajenran, Kazuhisa Seta, Jingyun Wang
PublisherAsia-Pacific Society for Computers in Education
Pages51-56
Number of pages6
ISBN (Electronic)9789869721479
Publication statusPublished - 22 Nov 2021
Event29th International Conference on Computers in Education Conference, ICCE 2021 - Virtual, Online
Duration: 22 Nov 202126 Nov 2021

Publication series

Name29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
Volume1

Conference

Conference29th International Conference on Computers in Education Conference, ICCE 2021
CityVirtual, Online
Period22/11/2126/11/21

Keywords

  • Filipino
  • linguistic features
  • natural language processing
  • Readability assessment

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Education

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