Year: 2017
Type: conference-paper
Abstract: 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.
DOI: 10.18653/v1/w17-1304
Authors: Claudia Borg, Albert Gatt
Institution: University of Malta
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2598288705