ConceptVector : Text Visual Analytics via Interactive Lexicon Building Using Word Embedding
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 resul...
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - 24(2018), 1 vom: 07. Jan., Seite 361-370 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2018
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics |
| Schlagworte: | Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't |
| Zusammenfassung: | 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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| Beschreibung: | Date Completed 08.04.2019 Date Revised 08.04.2019 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1941-0506 |
| DOI: | 10.1109/TVCG.2017.2744478 |