Abstract
Maltese is a prime example of a language that emerged through extensive language contact, joining the two linguistic worlds of Semitic and Italo-Romance languages. Previous studies have shown this shared origin on the basis of hands-on comparative methods. However, such approaches may be biased by the researchers perspective and the selected material. To avoid this bias, we employed a naive computational method that classifies words on the basis of their phonotactics. Specifically, we trained a simple two-layer neural network on Tunisian and Italian nouns, i.e. the languages that Maltese emerged from. We used the trained network to classify Maltese nouns based on their phonotactic characteristics as either of Tunisian or of Italian origin. Overall, the network is capable of correctly classifying Maltese nouns as belonging to either of the original languages. Moreover, we find that the classification depends on whether a noun has a sound or broken plural. By manipulating the segment identity in the training input, we found that consonants are more important for the classification of Maltese nouns than vowels. While our results replicate previous comparative studies, they also demonstrate that a more fine grained classification of a language's origin can be based on individual words and morphological classes.