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Readability of Arabic Medicine Information Leaflets: A Machine Learning Approach
- Source :
- Procedia Computer Science. 82:122-126
- Publication Year :
- 2016
- Publisher :
- Elsevier BV, 2016.
-
Abstract
- This paper presents a project that explores the possibility of assessing the readability level of Arabic medicine information leaflets using machine learning techniques. There are a number of popular readability formulas and tools that have been successfully used to assess the readability of health-related information in several languages. However, there is limited work on the readability assessment of health-related information, specifically medicine information leaflets in Arabic. We describe the design of a tool that uses machine learning to assess the readability of medicine information leaflets. We utilize a corpus comprising 1112 medicine information leaflets annotated with three difficulty levels. Based on a study of existing literature, we selected a number of features influencing text difficulty. The tool will help specialized organizations in medicine information leaflets production to produce the leaflets at appropriate level of reading for the majority of leaflets consumers.
- Subjects :
- Computer science
Arabic
media_common.quotation_subject
02 engineering and technology
computer.software_genre
Machine learning
Machine Learning
03 medical and health sciences
0302 clinical medicine
Reading (process)
0202 electrical engineering, electronic engineering, information engineering
030212 general & internal medicine
cardiovascular diseases
Arabic Language
General Environmental Science
media_common
Arabic Natural Language Processing
Multimedia
business.industry
technology, industry, and agriculture
Readability
language.human_language
Medicine Information Leaflets
Text Readability
language
cardiovascular system
General Earth and Planetary Sciences
020201 artificial intelligence & image processing
lipids (amino acids, peptides, and proteins)
Artificial intelligence
business
computer
Natural language processing
Subjects
Details
- ISSN :
- 18770509
- Volume :
- 82
- Database :
- OpenAIRE
- Journal :
- Procedia Computer Science
- Accession number :
- edsair.doi.dedup.....5f40aeb823244e1a8ec53091b474b1a4
- Full Text :
- https://doi.org/10.1016/j.procs.2016.04.017