  Significant work has been done on learning regular expressions from a set of
data values. Depending on the domain, this approach can be very successful.
However, significant time is required to learn these expressions and the
resulting expressions can become either very complex or inaccurate in the
presence of dirty data. The alternative of manually writing regular expressions
becomes unattractive when faced with a large number of values that must be
matched.
  As an alternative, we propose learning from a large corpus of manually
authored, but uncurated regular expressions mined from a public repository. The
advantage of this approach is that we are able to extract salient features from
a set of strings with limited overhead to feature engineering. Since the set of
regular expressions covers a wide range of application domains, we expect them
to be widely applicable.
  To demonstrate the potential effectiveness of our approach, we train a model
using the extracted corpus of regular expressions for the class of semantic
type classification. While our approach yields results that are overall
inferior to the state-of-the-art, our feature extraction code is an order of
magnitude smaller, and our model outperforms a popular existing approach on
some classes. We also demonstrate the possibility of using uncurated regular
expressions for unsupervised learning.
