POTATO: exPlainable infOrmation exTrAcTion framewOrk

@article{Kovacs2022POTATOEI,
  title={POTATO: exPlainable infOrmation exTrAcTion framewOrk},
  author={Adam Kovacs and Kinga G'emes and Eszter Ikl'odi and G{\'a}bor Recski},
  journal={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
  year={2022}
}
  • Adam KovacsKinga G'emes Gábor Recski
  • Published 31 January 2022
  • Computer Science
  • Proceedings of the 31st ACM International Conference on Information & Knowledge Management
We present POTATO, a task- and language-independent 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 web-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… 
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