  Information Extraction (IE) aims to automatically generate a large knowledge
base from natural language text, but progress remains slow. Supervised learning
requires copious human annotation, while unsupervised and weakly supervised
approaches do not deliver competitive accuracy. As a result, most fielded
applications of IE, as well as the leading TAC-KBP systems, rely on significant
amounts of manual engineering. Even "Extreme" methods, such as those reported
in Freedman et al. 2011, require about 10 hours of expert labor per relation.
  This paper shows how to reduce that effort by an order of magnitude. We
present a novel system, InstaRead, that streamlines authoring with an ensemble
of methods: 1) encoding extraction rules in an expressive and compositional
representation, 2) guiding the user to promising rules based on corpus
statistics and mined resources, and 3) introducing a new interactive
development cycle that provides immediate feedback --- even on large datasets.
Experiments show that experts can create quality extractors in under an hour
and even NLP novices can author good extractors. These extractors equal or
outperform ones obtained by comparably supervised and state-of-the-art
distantly supervised approaches.
