  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.
