Conference resolution using decision trees

Abstract

Corefence 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 pessimisitc error pruning method gives better generalization in a coreference resolution task than that reported in Soon et al. [20], when weights of positive and negative examples are properly chosen.