Skip to main content
This website is in trial operation, support us!
We gratefully acknowledge support from all contributors.
Contribute Donate

Computer Science > Computation and Language

arXiv:2306.03774v4 (cs)
[Submitted on 6 Jun 2023 (v1) , last revised 4 Sep 2025 (this version, v4)]

Title: Exploring Linguistic Features for Turkish Text Readability

Title: 探索土耳其语文本可读性的语言特征

Authors:Ahmet Yavuz Uluslu, Gerold Schneider
Abstract: 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.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2306.03774 [cs.CL]
  (or arXiv:2306.03774v4 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2306.03774

Submission history

From: Ahmet Yavuz Uluslu [view email]
[v1] Tue, 6 Jun 2023 15:32:22 UTC (6,967 KB)
[v2] Sun, 25 Jun 2023 12:57:37 UTC (6,967 KB)
[v3] Sat, 4 Nov 2023 13:03:35 UTC (6,968 KB)
[v4] Thu, 4 Sep 2025 11:06:14 UTC (6,951 KB)
Full-text links:

Access Paper:

view license
Current browse context:
cs.CL
< prev   |   next >
new | recent | 2023-06
Change to browse by:
cs

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)