We present a supervised learning approach to identification of anaphoric and non-anaphoric noun phrases and show how such information can be incorporated into a coreference resolution system. The resulting system outperforms the best MUC-6 and MUC-7 coreference resolution systems on the corresponding MUC coreference data sets, obtaining F-measures of 66.2 and 64.0, respectively.