Painless Semi-Supervised Morphological Segmentation using Conditional Random Fields

We discuss data-driven morphological segmentation, in which word forms are segmented into morphs, that is the surface forms of morphemes. We extend a recent segmentation approach based on conditional random fields from purely supervised to semi-supervised learning by exploiting available unsupervised segmentation techniques. We integrate the unsupervised techniques into the conditional random field model via feature set augmentation. Experiments on three diverse languages show that this straightforward semi-supervised extension greatly improves the segmentation accuracy of the purely supervised CRFs in a computationally efficient manner.