Testing Semantic Dominance in Mian Gender: Three Machine Learning Models

Abstract

The Trans-New Guinea language Mian has a four-valued gender system that has been analyzed in detail as semantic. This means that the principles of gender assignment are based on the meaning of the noun. Languages with purely semantic systems are at one end of a spectrum of possible assignment types, while others are assumed to have both semantic and formal (i.e., phonology or morphology-based) assignment. Given the possibility of gender assignment by both semantic and formal principles, it is worthwhile testing the empirical validity of the categorization of the Mian system as predominantly semantic. Here, we apply three machine learning models to determine independently what role semantics and phonology play in predicting Mian gender. Information about the formal and semantic features of nouns is extracted automatically from a dictionary. Different types of computational classifiers are trained to predict the grammatical gender of nouns, and the performance of the computational classifiers is used to assess the relevance of form and semantics in relation to gender prediction. The results show that semantics is dominant in predicting the gender of nouns in Mian. While it validates the original analysis of the Mian system, it also provides further evidence that claims of an equal contribution of form-based and semantic features in gender assignment do not hold for at least a proper subset of languages with gender.