Understanding scholarly Natural Language Processing system diagrams through application of the Richards-Engelhardt framework
This work addresses the problem of interpreting complex NLP system diagrams for researchers, but it is incremental as it builds on an existing framework.
The authors applied the Richards-Engelhardt framework to analyze diagrams of Natural Language Processing systems from scholarly works, finding it effective for reflection and proposing new vocabulary and visual encoding principles.
We utilise Richards-Engelhardt framework as a tool for understanding Natural Language Processing systems diagrams. Through four examples from scholarly proceedings, we find that the application of the framework to this ecological and complex domain is effective for reflecting on these diagrams. We argue for vocabulary to describe multiple-codings, semiotic variability, and inconsistency or misuse of visual encoding principles in diagrams. Further, for application to scholarly Natural Language Processing systems, and perhaps systems diagrams more broadly, we propose the addition of "Grouping by Object" as a new visual encoding principle, and "Emphasising" as a new visual encoding type.