Self-Supervised Chinese Word Segmentation
We propose a new unsupervised training method for acquiring probability models that accurately segment Chinese character sequences into words. By constructing a core lexicon to guide unsupervised word learning, self-supervised segmentation overcomes the local maxima problems that hamper standard EM training. Our procedure uses successive EM phases to learn a good probability model over character strings, and then prunes this model with a mutual information selection criterion to obtain a more accurate word lexicon. The segmentations produced by these models are more accurate than those produced by training with EM alone.