Morphological Analysis for the Maltese Language: The Challenges of a Hybrid System
This addresses the problem of morphological processing for Maltese, a specific language, and is incremental as it applies existing methods to analyze hybridity.
The paper tackled the challenges of morphological analysis for Maltese, a hybrid language with both concatenative and non-concatenative processes, by evaluating machine learning techniques on morphological labelling and clustering, finding differences in performance between the two systems.
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.