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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TVCG.2017.2744478
|2 doi
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|a pubmed24n0918.xml
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|a eng
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|a Park, Deokgun
|e verfasserin
|4 aut
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|a ConceptVector
|b Text Visual Analytics via Interactive Lexicon Building Using Word Embedding
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|c 2018
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|a Date Completed 08.04.2019
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|a Date Revised 08.04.2019
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Central to many text analysis methods is the notion of a concept: a set of semantically related keywords characterizing a specific object, phenomenon, or theme. Advances in word embedding allow building a concept from a small set of seed terms. However, naive application of such techniques may result in false positive errors because of the polysemy of natural language. To mitigate this problem, we present a visual analytics system called ConceptVector that guides a user in building such concepts and then using them to analyze documents. Document-analysis case studies with real-world datasets demonstrate the fine-grained analysis provided by ConceptVector. To support the elaborate modeling of concepts, we introduce a bipolar concept model and support for specifying irrelevant words. We validate the interactive lexicon building interface by a user study and expert reviews. Quantitative evaluation shows that the bipolar lexicon generated with our methods is comparable to human-generated ones
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|a Journal Article
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|a Research Support, N.I.H., Extramural
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|a Research Support, Non-U.S. Gov't
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700 |
1 |
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|a Kim, Seungyeon
|e verfasserin
|4 aut
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1 |
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|a Lee, Jurim
|e verfasserin
|4 aut
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700 |
1 |
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|a Choo, Jaegul
|e verfasserin
|4 aut
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700 |
1 |
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|a Diakopoulos, Nicholas
|e verfasserin
|4 aut
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1 |
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|a Elmqvist, Niklas
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 24(2018), 1 vom: 07. Jan., Seite 361-370
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|g year:2018
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|g day:07
|g month:01
|g pages:361-370
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|u http://dx.doi.org/10.1109/TVCG.2017.2744478
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