Visualization Techniques to Enhance Automated Event Extraction
This work addresses the challenge of interpreting high-dimensional text data in event extraction tasks for researchers and practitioners in NLP, but it is incremental as it applies existing visualization techniques to a specific domain.
The paper tackled the problem of visualizing complex data to enhance automated event extraction, specifically for identifying triggers of state-led mass killings from news articles using NLP, and demonstrated that visualizations can aid in exploratory analysis, training, and validation stages.
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.