  Maltese is a morphologically rich language with a hybrid morphological system
which features both concatenative and non-concatenative processes. This paper
analyses the impact of this hybridity on the performance of machine learning
techniques for morphological labelling and clustering. In particular, we
analyse a dataset of morphologically related word clusters to evaluate the
difference in results for concatenative and nonconcatenative clusters. We also
describe research carried out in morphological labelling, with a particular
focus on the verb category. Two evaluations were carried out, one using an
unseen dataset, and another one using a gold standard dataset which was
manually labelled. The gold standard dataset was split into concatenative and
non-concatenative to analyse the difference in results between the two
morphological systems.
