# Diverse Linguistic Features for Assessing Reading Difficulty of Educational Filipino Texts

arXiv: https://arxiv.org/abs/2108.00241 · HTML: https://mlsanity.com/paper/2108.00241/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 31 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [Application of Lexical Features Towards Improvement of Filipino Readability Identification of Children's Literature](https://mlsanity.com/paper/2101.10537/) (2021)

## Built on by (most cited first, 5 of 5)

- [A Baseline Readability Model for Cebuano](https://mlsanity.com/paper/2203.17225/) (2022)
- [Automatic Readability Assessment for Closely Related Languages](https://mlsanity.com/paper/2305.13478/) (2023)
- [Uniform Complexity for Text Generation](https://mlsanity.com/paper/2204.05185/) (2022)
- [Under the Microscope: Interpreting Readability Assessment Models for Filipino](https://mlsanity.com/paper/2110.00157/) (2021)
- [BasahaCorpus: An Expanded Linguistic Resource for Readability Assessment in Central Philippine Languages](https://mlsanity.com/paper/2310.11584/) (2023)

## Abstract (arXiv)

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.