  Only a year ago, all state-of-the-art coreference resolvers were using an
extensive amount of surface features. Recently, there was a paradigm shift
towards using word embeddings and deep neural networks, where the use of
surface features is very limited. In this paper, we show that a simple SVM
model with surface features outperforms more complex neural models for
detecting anaphoric mentions. Our analysis suggests that using generalized
representations and surface features have different strength that should be
both taken into account for improving coreference resolution.
