Unsupervised discovery of morphemes

We present two methods for unsupervised segmentation of words into morpheme-like units. The model utilized is espe-cially suited for languages with a rich morphology, such as Finnish. The first method is based on the Minimum Descrip-tion Length (MDL) principle and works online. In the second method, Max-imum Likelihood (ML) optimization is used. The quality of the segmentations is measured using an evaluation method that compares the segmentations produced to an existing morphological analysis. Ex-periments on both Finnish and English corpora show that the presented methods perform well compared to a current state-of-the-art system.