  The German plural system has become a focal point for conflicting theories of
language, both linguistic and cognitive. We present simulation results with
three simple classifiers - an ordinary nearest neighbour algorithm, Nosofsky's
`Generalized Context Model' (GCM) and a standard, three-layer backprop network
- predicting the plural class from a phonological representation of the
singular in German. Though these are absolutely `minimal' models, in terms of
architecture and input information, they nevertheless do remarkably well. The
nearest neighbour predicts the correct plural class with an accuracy of 72% for
a set of 24,640 nouns from the CELEX database. With a subset of 8,598
(non-compound) nouns, the nearest neighbour, the GCM and the network score
71.0%, 75.0% and 83.5%, respectively, on novel items. Furthermore, they
outperform a hybrid, `pattern-associator + default rule', model, as proposed by
Marcus et al. (1995), on this data set.
