Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization
Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving spatializations for text corpora as two-dimensional scatter plots, reflecting s...
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Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - 30(2024), 1 vom: 23. Jan., Seite 902-912
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| 1. Verfasser: |
Atzberger, Daniel
(VerfasserIn) |
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| Weitere Verfasser: |
Cech, Tim,
Trapp, Matthias,
Richter, Rico,
Scheibel, Willy,
Dollner, Jurgen,
Schreck, Tobias |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics
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| Schlagworte: | Journal Article |
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