  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.
