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

• URL: http://arxiv.org/abs/2104.03958v1
• Date: Thu, 8 Apr 2021 17:58:03 GMT
• Title: GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns
• Authors: Piyawat Lertvittayakumjorn, Leshem Choshen, Eyal Shnarch, Francesca Toni
• Abstract summary: We provide a Python library for GrASP, an algorithm for drawing patterns from textual data. The library is equipped with a web-based interface empowering human users to conveniently explore the data and the extracted patterns.
• Score: 25.350957495556226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data exploration is an important step of every data science and machine learning project, including those involving textual data. We provide a Python library for GrASP, an existing algorithm for drawing patterns from textual data. The library is equipped with a web-based interface empowering human users to conveniently explore the data and the extracted patterns. We also demonstrate the use of the library in two settings (spam detection and argument mining) and discuss future deployments of the library, e.g., beyond textual data exploration.

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