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Maltese is a morphologically rich language with a hybrid morphological systemwhich features both concatenative and non-concatenative processes. This paperanalyses the impact of this hybridity on the performance of machine learningtechniques for morphological labelling and clustering. In particular, weanalyse a dataset of morphologically related word clusters to evaluate thedifference in results for concatenative and nonconcatenative clusters. We alsodescribe research carried out in morphological labelling, with a particularfocus on the verb category. Two evaluations were carried out, one using anunseen dataset, and another one using a gold standard dataset which wasmanually labelled. The gold standard dataset was split into concatenative andnon-concatenative to analyse the difference in results between the twomorphological systems.

Morphological Analysis for the Maltese Language: The Challenges of a Hybrid System