Integrating LSA-based hierarchical conceptual space and machine learning methods for leveling the readability of domain-specific texts
Abstract
Text readability assessment is a challenging interdisciplinary endeavor with rich practical implications. It has long drawn the attention of researchers internationally, and the readability models since developed have been widely applied to various fields. Previous readability models have only made use of linguistic features employed for general text analysis and have not been sufficiently accurate when used to gauge domain-specific texts. In...
Paper Details
Title
Integrating LSA-based hierarchical conceptual space and machine learning methods for leveling the readability of domain-specific texts
Published Date
Apr 5, 2019
Journal
Volume
25
Issue
3
Pages
331 - 361
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History