A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney,…

This study applies the Apriori algorithm to analyse patterns, syntactic structures, and thematic clusters in Chinese studies data from various genres. This study aims to identify recurring linguistic elements in order to shed light on the dynamic nature of the Chinese language across different contexts and time periods. The Apriori algorithm is…

The study aimed to investigate the disciplinary variations and the structural/functional features of recurrent expressions in the introduction and literature sections of the Master of Arts (MA) theses in two corpora. It is significant since the bundles fulfil specific functions in the form of formulaic language. The sampling of the study was…

Iterated learning models of language evolution have typically been used to study the emergence of language, rather than historical language change. We use iterated learning models to investigate historical change in the accent classes of two Korean dialects. Simulations reveal that many of the patterns of historical change can be explained as…

Understanding how native speakers acquire the phonological patterns in their language is a key task for the field of phonology. Numerous studies have suggested that phonological learning is a biased process: certain phonological patterns are easily accessed and learned by the speakers, while others show acquisition difficulties. These differences…

The present paper addresses the study of non-arbitrariness in language within a deep learning framework. We present a set of experiments aimed at assessing the pervasiveness of different forms of non-arbitrary phonological patterns across a set of typologically distant languages. Different sequence-processing neural networks are trained in a set…