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.

@inproceedings{garbin2005disambiguating, author = {Garbin, Eric and Mani, Inderjeet}, title = {Disambiguating toponyms in news}, booktitle = {Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing}, publisher = {Association for Computational Linguistics}, address = {Stroudsburg, PA, USA}, year = {2005}, pages = {363--370}, url = {http://dx.doi.org/10.3115/1220575.1220621}, doi = {10.3115/1220575.1220621}, keywords = {geo, toponym, news, disambiguation, map, extraction}, abstract = {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.} }

%0 = inproceedings %A = Garbin, Eric and Mani, Inderjeet %B = Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing %C = Stroudsburg, PA, USA %D = 2005 %I = Association for Computational Linguistics %T = Disambiguating toponyms in news %U = http://dx.doi.org/10.3115/1220575.1220621