Paper: Efficient Higher-Order CRFs for Morphological Tagging

ACL ID D13-1032
Title Efficient Higher-Order CRFs for Morphological Tagging
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

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 morpho- logical properties and that across languages higher-order models give significant improve- ments over 1st-order models.