Maltese as a Merger of Two Worlds: A cross-language approach to phonotactic classification

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. The present study aims at demonstrating this shared origin by employing computational methods. For this, we trained a simple two-layer neural network on Tunisian and Italian nouns, i.e. thelanguages that Maltese emerged from. We used the trained network to classify Maltese nouns 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 forms a sound or broken plural. 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.