GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns

Understand

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.