  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.
