POTATO: exPlainable infOrmation exTrAcTion framewOrk

TL;DR

POTATO is a versatile, human-in-the-loop framework that enables rule-based text classification across languages and domains by leveraging graph-based features and interpretable machine learning.

Contribution

It introduces a novel, language- and task-independent system for rule extraction using graph representations and real-time user interaction.

Findings

Supports multiple graph formats like AMR, UD, 4lang

Applied successfully to legal and social media texts

Provides real-time rule suggestions and refinements

Abstract

We present POTATO, a task- and languageindependent framework for human-in-the-loop (HITL) learning of rule-based text classifiers using graph-based features. POTATO handles any type of directed graph and supports parsing text into Abstract Meaning Representations (AMR), Universal Dependencies (UD), and 4lang semantic graphs. A streamlit-based user interface allows users to build rule systems from graph patterns, provides real-time evaluation based on ground truth data, and suggests rules by ranking graph features using interpretable machine learning models. Users can also provide patterns over graphs using regular expressions, and POTATO can recommend refinements of such rules. POTATO is applied in projects across domains and languages, including classification tasks on German legal text and English social media data. All components of our system are written in Python, can be installed via pip, and are released under an MIT License on GitHub.