  We describe the first automatic approach for merging coreference annotations
obtained from multiple annotators into a single gold standard. This merging is
subject to certain linguistic hard constraints and optimization criteria that
prefer solutions with minimal divergence from annotators. The representation
involves an equivalence relation over a large number of elements. We use Answer
Set Programming to describe two representations of the problem and four
objective functions suitable for different datasets. We provide two
structurally different real-world benchmark datasets based on the METU-Sabanci
Turkish Treebank and we report our experiences in using the Gringo, Clasp, and
Wasp tools for computing optimal adjudication results on these datasets.
