Efficient Higher-Order CRFs for Morphological Tagging

Thomas Müller, Helmut Schmid, Hinrich Schütze

Abstract: Training higher-order conditional random fields is prohibitive for huge tag sets. We present an approximated conditional random field using coarse-to-fine decoding and early updating. We show that our implementation yields fast and accurate morphological taggers across six languages with different morphological properties and that across languages higher-order models give significant improvements over 1-order models.