Unsupervised phonemic Chinese word segmentation using Adaptor Grammars
Mark S. Johnson, Katherine Demuth · 2010
Adaptor grammars are a framework for expressing and performing inference over a variety of non-parametric linguistic models. These models currently provide state-of-the-art performance on unsupervised word segmentation from phonemic representations of child-directed unsegmented English utterances. This paper investigates the applicability of these models to unsupervised word segmentation of Mandarin. We investigate a wide variety of different segmentation models, and show that the best segmentation accuracy is obtained from models that capture interword