UNSUPERVISED LEARNING USING GLOBAL FEATURES, INCLUDING FOR LOG-LINEAR MODEL WORD SEGMENTATION

Described is a technology for performing unsupervised learning using global features extracted from unlabeled examples. The unsupervised learning process may be used to train a log-linear model, such as for use in morphological segmentation of words. For example, segmentations of the examples are sampled based upon the global features to produce a segmented corpus and log-linear model, which are then iteratively reprocessed to produce a final segmented corpus and a log-linear model.