Exploring Linguistic Features for Turkish Text Readability
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Abstract (arXiv)
This paper presents the first comprehensive study on automatic readability assessment of Turkish texts. We combine state-of-the-art neural network models with linguistic features at lexical, morphological, syntactic and discourse levels to develop an advanced readability tool. We evaluate the effectiveness of traditional readability formulas compared to modern automated methods and identify key linguistic features that determine the readability of Turkish texts.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 52 references that are in the map. First posted on arXiv 6 Jun 2023.
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References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723
- 2019Supervised and Unsupervised Neural Approaches to Text Readabilitycited by 46
- 2021Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Featurescited by 36
- 2020Linguistic Features for Readability Assessmentcited by 35
- 2021Trends, Limitations and Open Challenges in Automatic Readability Assessment Researchcited by 28
- 2021BERT Embeddings for Automatic Readability Assessmentcited by 19
- 2023Traditional Readability Formulas Compared for Englishcited by 4
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