This research is aimed at the problem of disambiguating toponyms (place names) in terms of a classification derived by merging information from two publicly available gazetteers. To establish the dif- ficulty of the problem, we measured the degree of ambiguity, with respect to a gazetteer, for toponyms in news. We found that 67.82% of the toponyms found in a corpus that were ambiguous in a gaz- etteer lacked a local discriminator in the text. Given the scarcity of human- annotated data, our method used unsuper- vised machine learning to develop disam- biguation rules. Toponyms were automatically tagged with information about them found in a gazetteer. A toponym that was ambiguous in the gazet- teer was automatically disambiguated based on preference heuristics. This automatically tagged data was used to train a machine learner, which disambigu- ated toponyms in a human-annotated news corpus at 78.5% accuracy.