2026-09-19T01:15:33Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/531212022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
William J. Long.
Bafuka, Freddy Nole
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-03-25T15:03:53Z
2010-03-25T15:03:53Z
2009
2009
http://hdl.handle.net/1721.1/53121
503453538
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Includes bibliographical references (p. 70-71).
Many studies have shown that the readability of health documents presented to consumers does not match their reading levels. An accurate assessment of the readability of health-related texts is an important step in providing material that match readers' literacy. Current readability measurements depend heavily on text analysis (NLP), but neglect style (text layout). In this study, we show that style properties are important predictors of documents' readability. In particular, we build an automated computer program that uses documents' style to predict their readability score. The style features are extracted by analyzing only one page of the document as an image. The scores produced by our system were tested against scores given by human experts. Our tool shows stronger correlation to experts' scores than the Flesch-Kincaid readability grading method. We provide an end-user program, VisualGrader, which provides a Graphical User Interface to the scoring model.
by Freddy Nole Bafuka.
M.Eng.
71 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Beyond text analysis : image-based evaluation of health-related text readability using style features
Image-based evaluation of health-related text readability using style features
Image-based evaluation of readability of health-related documents
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f74a3bb3-fc8e-439d-b0c3-e55e4b551dc3">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Beyond text analysis : image-based evaluation of health-related text readability using style features</Title>
<Subtitle>Image-based evaluation of health-related text readability using style features</Subtitle>
<Subtitle>Image-based evaluation of readability of health-related documents</Subtitle>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2009</PublicationDate>
<Authors>
<Author>
<DisplayName>Bafuka, Freddy Nole</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>Many studies have shown that the readability of health documents presented to consumers does not match their reading levels. An accurate assessment of the readability of health-related texts is an important step in providing material that match readers' literacy. Current readability measurements depend heavily on text analysis (NLP), but neglect style (text layout). In this study, we show that style properties are important predictors of documents' readability. In particular, we build an automated computer program that uses documents' style to predict their readability score. The style features are extracted by analyzing only one page of the document as an image. The scores produced by our system were tested against scores given by human experts. Our tool shows stronger correlation to experts' scores than the Flesch-Kincaid readability grading method. We provide an end-user program, VisualGrader, which provides a Graphical User Interface to the scoring model.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>