  We present LEMMING, a modular log-linear model that jointly models
lemmatization and tagging and supports the integration of arbitrary global
features. It is trainable on corpora annotated with gold standard tags and
lemmata and does not rely on morphological dictionaries or analyzers. LEMMING
sets the new state of the art in token-based statistical lemmatization on six
languages; e.g., for Czech lemmatization, we reduce the error by 60%, from 4.05
to 1.58. We also give empirical evidence that jointly modeling morphological
tags and lemmata is mutually beneficial.
