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%0 Conference Proceedings
%A Garbin, Eric & Mani, Inderjeet
%D 2005
%T Disambiguating toponyms in news
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%B Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
%C Stroudsburg, PA, USA
%I Association for Computational Linguistics
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%P 363--370
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%F garbin2005disambiguating
%K geo, toponym, news, disambiguation, map, extraction
%X 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 difficulty 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 gazetteer lacked a local discriminator in the text. Given the scarcity of human-annotated data, our method used unsupervised machine learning to develop disambiguation rules. Toponyms were automatically tagged with information about them found in a gazetteer. A toponym that was ambiguous in the gazetteer was automatically disambiguated based on preference heuristics. This automatically tagged data was used to train a machine learner, which disambiguated toponyms in a human-annotated news corpus at 78.5% accuracy.
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%U http://dx.doi.org/10.3115/1220575.1220621
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