TL;DR
LightTag is a text annotation platform designed to optimize overall NLP process throughput by aligning with user goals beyond just creating labeled data, emphasizing workflow integration and efficiency.
Contribution
The paper introduces LightTag, a text annotation tool that focuses on enhancing NLP pipeline efficiency rather than solely annotator productivity, with detailed design rationale and user interface considerations.
Findings
Supports full NLP lifecycle integration
Improves annotation throughput at the process level
Aligns tool design with user goals beyond labeling
Abstract
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.
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