Coreference resolution is the process of determining whether two expressions in natural language refer to the same entity in the world. We adopt machine learning approach using decision tree to a coreference resolution of general noun phrases in unrestricted text based on well defined features. We also use approximate matching algorithms for a string match feature and databases of American last names and male and female first names for gender agreement and alias feature. For the evaluation we use MUC-6 coreference corpora. We show that pessimistic error pruning method gives better generalization in a coreference resolution task than that reported in W.M. Soon et al. (2001) when weights of positive and negative examples are properly chosen