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Visual Readability Analysis: How to Make Your Writings Easier to Read
May 2012 (vol. 18 no. 5)
pp. 662-674
ASCII Text
x 
 
D. Oelke, D. Spretke, A. Stoffel, D. A. Keim, "Visual Readability Analysis: How to Make Your Writings Easier to Read," IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 5, pp. 662-674, May, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2011.266,
author = {D. Oelke and D. Spretke and A. Stoffel and D. A. Keim},
title = {Visual Readability Analysis: How to Make Your Writings Easier to Read},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {18},
number = {5},
issn = {1077-2626},
year = {2012},
pages = {662-674},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2011.266},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Visual Readability Analysis: How to Make Your Writings Easier to Read
IS - 5
SN - 1077-2626
SP662
EP674
EPD - 662-674
A1 - D. Oelke,
A1 - D. Spretke,
A1 - A. Stoffel,
A1 - D. A. Keim,
PY - 2012
KW - text analysis
KW - learning (artificial intelligence)
KW - document processing
KW - visual readability analysis
KW - semiautomatic feature selection approach
KW - visual analysis tool
KW - visual representations
KW - VisRA
KW - draft version
KW - text processing
KW - Vocabulary
KW - Correlation
KW - Training data
KW - Length measurement
KW - Navigation
KW - Visual analytics
KW - feature evaluation and selection.
KW - Document and text processing
VL - 18
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
D. Oelke, Dept. of Comput. & Inf. Sci., Univ. of Konstanz, Konstanz, Germany
D. Spretke, Dept. of Comput. & Inf. Sci., Univ. of Konstanz, Konstanz, Germany
A. Stoffel, Dept. of Comput. & Inf. Sci., Univ. of Konstanz, Konstanz, Germany
D. A. Keim, Dept. of Comput. & Inf. Sci., Univ. of Konstanz, Konstanz, Germany
We present a tool that is specifically designed to support a writer in revising a draft version of a document. In addition to showing which paragraphs and sentences are difficult to read and understand, we assist the reader in understanding why this is the case. This requires features that are expressive predictors of readability, and are also semantically understandable. In the first part of the paper, we, therefore, discuss a semiautomatic feature selection approach that is used to choose appropriate measures from a collection of 141 candidate readability features. In the second part, we present the visual analysis tool VisRA, which allows the user to analyze the feature values across the text and within single sentences. Users can choose between different visual representations accounting for differences in the size of the documents and the availability of information about the physical and logical layout of the documents. We put special emphasis on providing as much transparency as possible to ensure that the user can purposefully improve the readability of a sentence. Several case studies are presented that show the wide range of applicability of our tool. Furthermore, an in-depth evaluation assesses the quality of the measure and investigates how well users do in revising a text with the help of the tool.

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Index Terms:
text analysis,learning (artificial intelligence),document processing,visual readability analysis,semiautomatic feature selection approach,visual analysis tool,visual representations,VisRA,draft version,text processing,Vocabulary,Correlation,Training data,Length measurement,Navigation,Visual analytics,feature evaluation and selection.,Document and text processing
Citation:
D. Oelke, D. Spretke, A. Stoffel, D. A. Keim, "Visual Readability Analysis: How to Make Your Writings Easier to Read," IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 5, pp. 662-674, May 2012, doi:10.1109/TVCG.2011.266
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