SnapToGrid: From Statistical to Interpretable Models for Biomedical Information Extraction

WS 2016 Marco A. Valenzuela-EscarcegaGus Hahn-PowellDane BellMihai Surdeanu

We propose an approach for biomedical information extraction that marries the advantages of machine learning models, e.g., learning directly from data, with the benefits of rule-based approaches, e.g., interpretability. Our approach starts by training a feature-based statistical model, then converts this model to a rule-based variant by converting its features to rules, and "snapping to grid" the feature weights to discrete votes... (read more)

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