  Humans do not make inferences over texts, but over models of what texts are
about. When annotators are asked to annotate coreferent spans of text, it is
therefore a somewhat unnatural task. This paper presents an alternative in
which we preprocess documents, linking entities to a knowledge base, and turn
the coreference annotation task -- in our case limited to pronouns -- into an
annotation task where annotators are asked to assign pronouns to entities.
Model-based annotation is shown to lead to faster annotation and higher
inter-annotator agreement, and we argue that it also opens up for an
alternative approach to coreference resolution. We present two new coreference
benchmark datasets, for English Wikipedia and English teacher-student
dialogues, and evaluate state-of-the-art coreference resolvers on them.
