mlsanityData to 14 Oct 2026

Visualization Techniques to Enhance Automated Event Extraction

arXiv 2106.06588 ↗First posted 11 Jun 2021cs.CL cs.GRcited by 3

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Abstract (arXiv)

Robust visualization of complex data is critical for the effective use of NLP for event classification, as the volume of data is large and the high-dimensional structure of text makes data challenging to summarize succinctly. In event extraction tasks in particular, visualization can aid in understanding and illustrating the textual relationships from which machine learning tools produce insights. Through our case study which seeks to identify potential triggers of state-led mass killings from news articles using NLP, we demonstrate how visualizations can aid in each stage, from exploratory analysis of raw data, to machine learning training analysis, and finally post-inference validation.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 3 of its 20 references that are in the map. First posted on arXiv 11 Jun 2021.

Builds on

3 of 20 references.

References · 3

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  3. 2017Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspectivecited by 36

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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 0; 2025: 1.

3 papers.