Dsolve—morphological segmentation for german using conditional random fields

Abstract

We describe Dsolve, a system for the segmentation of morphologically complex German words into their constituent morphs. Our approach treats morphological segmentation as a classification task, in which the locations and types of morph boundaries are predicted by a Conditional Random Field model trained from manually annotated data. The prediction of morph-boundary types in addition to their locations distinguishes Dsolve from similar approaches previously suggested in the literature.We show that the use of boundary types provides a (somewhat counter-intuitive) performance boost with respect to the simpler task of predicting only segment location