  This study addresses a series of methodological questions that arise when
modeling inflectional morphology with Linear Discriminative Learning. Taking
the semi-productive German noun system as example, we illustrate how decisions
made about the representation of form and meaning influence model performance.
We clarify that for modeling frequency effects in learning, it is essential to
make use of incremental learning rather than the endstate of learning. We also
discuss how the model can be set up to approximate the learning of inflected
words in context. In addition, we illustrate how in this approach the wug task
can be modeled in considerable detail. In general, the model provides an
excellent memory for known words, but appropriately shows more limited
performance for unseen data, in line with the semi-productivity of German noun
inflection and generalization performance of native German speakers.
