  While humans can extract information from unstructured text with high
precision and recall, this is often too time-consuming to be practical.
Automated approaches, on the other hand, produce nearly-immediate results, but
may not be reliable enough for high-stakes applications where precision is
essential. In this work, we consider the benefits and drawbacks of various
human-only, human-machine, and machine-only information extraction approaches.
We argue for the utility of a human-in-the-loop approach in applications where
high precision is required, but purely manual extraction is infeasible. We
present a framework and an accompanying tool for information extraction using
weak-supervision labelling with human validation. We demonstrate our approach
on three criminal justice datasets. We find that the combination of computer
speed and human understanding yields precision comparable to manual annotation
while requiring only a fraction of time, and significantly outperforms fully
automated baselines in terms of precision.
