  Modal sense classification (MSC) is a special WSD task that depends on the
meaning of the proposition in the modal's scope. We explore a CNN architecture
for classifying modal sense in English and German. We show that CNNs are
superior to manually designed feature-based classifiers and a standard NN
classifier. We analyze the feature maps learned by the CNN and identify known
and previously unattested linguistic features. We benchmark the CNN on a
standard WSD task, where it compares favorably to models using
sense-disambiguated target vectors.
