LightTag: Text Annotation Platform
This work addresses a practical problem for NLP practitioners and businesses by shifting annotation tool design from annotator productivity to overall process efficiency, though it appears incremental as it builds on existing annotation platforms.
The paper tackles the misalignment between text annotation tools' focus on labeled corpus creation and users' goal of delivering business value through NLP, introducing LightTag as a tool optimized for global NLP process throughput. It presents design rationale, data modeling, and user interface decisions that support the full NLP lifecycle.
Text annotation tools assume that their user's goal is to create a labeled corpus. However, users view annotation as a necessary evil on the way to deliver business value through NLP. Thus an annotation tool should optimize for the throughput of the global NLP process, not only the productivity of individual annotators. LightTag is a text annotation tool designed and built on that principle. This paper shares our design rationale, data modeling choices, and user interface decisions then illustrates how those choices serve the full NLP lifecycle.