  Proper identification of grade levels of children's reading materials is an
important step towards effective learning. Recent studies in readability
assessment for the English domain applied modern approaches in natural language
processing (NLP) such as machine learning (ML) techniques to automate the
process. There is also a need to extract the correct linguistic features when
modeling readability formulas. In the context of the Filipino language, limited
work has been done [1, 2], especially in considering the language's lexical
complexity as main features. In this paper, we explore the use of lexical
features towards improving the development of readability identification of
children's books written in Filipino. Results show that combining lexical
features (LEX) consisting of type-token ratio, lexical density, lexical
variation, foreign word count with traditional features (TRAD) used by previous
works such as sentence length, average syllable length, polysyllabic words,
word, sentence, and phrase counts increased the performance of readability
models by almost a 5% margin (from 42% to 47.2%). Further analysis and ranking
of the most important features were shown to identify which features contribute
the most in terms of reading complexity.
