  We introduce an automatic system that achieves state-of-the-art results on
the Winograd Schema Challenge (WSC), a common sense reasoning task that
requires diverse, complex forms of inference and knowledge. Our method uses a
knowledge hunting module to gather text from the web, which serves as evidence
for candidate problem resolutions. Given an input problem, our system generates
relevant queries to send to a search engine, then extracts and classifies
knowledge from the returned results and weighs them to make a resolution. Our
approach improves F1 performance on the full WSC by 0.21 over the previous best
and represents the first system to exceed 0.5 F1. We further demonstrate that
the approach is competitive on the Choice of Plausible Alternatives (COPA)
task, which suggests that it is generally applicable.
