Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TVCG.2011.266 | Visual Analytics Science and Technology |
Keywords | Field | DocType |
visual analysis tool,different visual representations accounting,case study,appropriate measure,draft version,candidate readability feature,feature value,visual readability analysis,writings easier,semiautomatic feature selection approach,in-depth evaluation,expressive predictor,writing,linguistics,visual analytics,text analysis,navigation,comprehension,visual analysis,correlation,document processing,training data,reading,length measurement,feature selection,computer graphics,learning artificial intelligence | Feature selection,Computer science,Visual analytics,Artificial intelligence,Natural language processing,Text processing,Transparency (graphic),Computer vision,Information retrieval,Document processing,Readability,Vocabulary,Sentence | Journal |
Volume | Issue | ISSN |
18 | 5 | 1941-0506 |
ISBN | Citations | PageRank |
978-1-4244-9487-3 | 10 | 0.57 |
References | Authors | |
18 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniela Oelke | 1 | 225 | 13.18 |
| David Spretke | 2 | 41 | 2.88 |
| Andreas Stoffel | 3 | 229 | 11.66 |
| Daniel A. Keim | 4 | 7704 | 1141.60 |