  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. In doing so, our proposal takes
advantage of the large body of work in machine learning, but it produces an
interpretable model, which can be directly edited by experts. We evaluate our
approach on the BioNLP 2009 event extraction task. Our results show that there
is a small performance penalty when converting the statistical model to rules,
but the gain in interpretability compensates for that: with minimal effort,
human experts improve this model to have similar performance to the statistical
model that served as starting point.
