  Data exploration is an important step of every data science and machine
learning project, including those involving textual data. We provide a novel
language tool, in the form of a publicly available Python library for
extracting patterns from textual data. The library integrates a first public
implementation of the existing GrASP algorithm. It allows users to extract
patterns using a number of general-purpose built-in linguistic attributes (such
as hypernyms, part-of-speech tags, and syntactic dependency tags), as envisaged
for the original algorithm, as well as domain-specific custom attributes which
can be incorporated into the library by implementing two functions. The library
is equipped with a web-based interface empowering human users to conveniently
explore data via the extracted patterns, using complementary pattern-centric
and example-centric views: the former includes a reading in natural language
and statistics of each extracted pattern; the latter shows applications of each
extracted pattern to training examples. We demonstrate the usefulness of the
library in classification (spam detection and argument mining), model analysis
(machine translation), and artifact discovery in datasets (SNLI and
20Newsgroups).
