More than Just Statistical Recurrence: Human and Machine Unsupervised Learning of M\=aori Word Segmentation across Morphological Processes

arXiv:2403.14444v1 Announce Type: new
Abstract: Non-M\=aori-speaking New Zealanders (NMS)are able to segment M\=aori words in a highlysimilar way to fluent speakers (Panther et al.,2024). This ability is assumed to derive through the identification and extraction of statistically recurrent forms. We examine this assumption by asking how NMS segmentations compare to those produced by Morfessor, an unsupervised machine learning model that operates based on statistical recurrence, across words formed by a variety of morphological processes. Both NMS and Morfessor succeed in …

abstract arxiv cs.cl extraction forms human human and machine identification machine processes segment segmentation speakers speaking statistical through type unsupervised unsupervised learning word words