Summary of A Large-scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions For Text Spatializations, by Daniel Atzberger et al.
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations
by Daniel Atzberger, Tim Cech, Willy Scheibel, Jürgen Döllner, Michael Behrisch, Tobias Schreck
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research visualizes semantic similarity between documents in a text corpus using two-dimensional scatterplot layouts, which depend on dimensionality reduction and latent embedding techniques. The study investigates the stability of these layouts under changes in text corpora, hyperparameters, and randomness in initialization. A sensitivity analysis is conducted through data measurement and analysis, quantifying layout similarity using ten metrics. The results provide guidelines for informed decisions on layout algorithms and highlight specific hyperparameter settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to visualize how similar documents are in a text corpus. It uses special techniques to reduce the complexity of the data and create a map-like view. The study looks at how this visualization changes when different things happen, like using different texts or adjusting certain settings. By measuring and analyzing the results, researchers can learn more about what makes these visualizations stable or unstable. |
Keywords
* Artificial intelligence * Dimensionality reduction * Embedding * Hyperparameter
