Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization

Atzberger, Daniel; Cech, Tim; Trapp, Matthias; Richter, Rico; Scheibel, Willy; Döllner, Jurgen; Schreck, Tobias

doi:10.1109/tvcg.2023.3326569

Cited by 5 publications

(1 citation statement)

References 66 publications

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“…To visualize word vectors, dimensionality reduction techniques are employed to transform them into lower-dimensional vectors. t-Distributed stochastic neighbor embedding (t-SNE) [42,43] is a nonlinear dimensionality reduction algorithm. Its primary objective is to preserve the local similarities between high-dimensional data points and maintain these relationships as much as possible in the lower-dimensional space.…”

Section: Cluster Analysis Of Disaster Metadatamentioning

confidence: 99%

Automatic Extraction and Cluster Analysis of Natural Disaster Metadata Based on the Unified Metadata Framework

Wang,

Shi,

Yang

et al. 2024

IJGI

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The development of information technology has led to massive, multidimensional, and heterogeneously sourced disaster data. However, there’s currently no universal metadata standard for managing natural disasters. Common pre-training models for information extraction requiring extensive training data show somewhat limited effectiveness, with limited annotated resources. This study establishes a unified natural disaster metadata standard, utilizes self-trained universal information extraction (UIE) models and Python libraries to extract metadata stored in both structured and unstructured forms, and analyzes the results using the Word2vec-Kmeans cluster algorithm. The results show that (1) the self-trained UIE model, with a learning rate of 3 × 10−4 and a batch_size of 32, significantly improves extraction results for various natural disasters by over 50%. Our optimized UIE model outperforms many other extraction methods in terms of precision, recall, and F1 scores. (2) The quality assessments of consistency, completeness, and accuracy for ten tables all exceed 0.80, with variances between the three dimensions being 0.04, 0.03, and 0.05. The overall evaluation of data items of tables also exceeds 0.80, consistent with the results at the table level. The metadata model framework constructed in this study demonstrates high-quality stability. (3) Taking the flood dataset as an example, clustering reveals five main themes with high similarity within clusters, and the differences between clusters are deemed significant relative to the differences within clusters at a significance level of 0.01. Overall, this experiment supports effective sharing of disaster data resources and enhances natural disaster emergency response efficiency.

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Section: Cluster Analysis Of Disaster Metadatamentioning

confidence: 99%

Automatic Extraction and Cluster Analysis of Natural Disaster Metadata Based on the Unified Metadata Framework

Wang,

Shi,

Yang

et al. 2024

IJGI

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A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations

Atzberger,

Cech,

Scheibel

et al. 2025

IEEE Trans. Visual. Comput. Graphics

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An Experimental Evaluation of Viewpoint‐Based 3D Graph Drawing

van Wageningen,

Mchedlidze,

Telea

2024

Computer Graphics Forum

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Node‐link diagrams are a widely used metaphor for creating visualizations of relational data. Most frequently, such techniques address creating 2D graph drawings, which are easy to use on computer screens and in print. In contrast, 3D node‐link graph visualizations are far less used, as they have many known limitations and comparatively few well‐understood advantages. A key issue here is that such 3D visualizations require users to select suitable viewpoints. We address this limitation by studying the ability of layout techniques to produce high‐quality views of 3D graph drawings. For this, we perform a thorough experimental evaluation, comparing 3D graph drawings, rendered from a covering sampling of all viewpoints, with their 2D counterparts across various state‐of‐the‐art node‐link drawing algorithms, graph families, and quality metrics. Our results show that, depending on the graph family, 3D node‐link diagrams can contain a many viewpoints that yield 2D visualizations that are of higher quality than those created by directly using 2D node‐link diagrams. This not only sheds light on the potential of 3D node‐link diagrams but also gives a simple approach to produce high‐quality 2D node‐link diagrams.

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Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization

Cited by 5 publications

References 66 publications

Automatic Extraction and Cluster Analysis of Natural Disaster Metadata Based on the Unified Metadata Framework

Automatic Extraction and Cluster Analysis of Natural Disaster Metadata Based on the Unified Metadata Framework

A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations

An Experimental Evaluation of Viewpoint‐Based 3D Graph Drawing

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