Semi-supervised learning of geographical gazetteers from the internet Export

@inproceedings{5732, abstract = {In this paper we present an approach to the acquisition of geographical gazetteers. Instead of creating these resources manually, we propose to extract gazetteers from the World Wide Web, using Data Mining techniques.The bootstrapping approach, investigated in our study, allows us to create new gazetteers using only a small seed dataset (1260 words). In addition to gazetteers, the system produces classifiers. They can be used online to determine a class (CITY, ISLAND, RIVER, MOUNTAIN, REGION, COUNTRY) of any geographical name. Our classifiers perform with the average accuracy of 86.5\%.}, author = {Uryupina, Olga}, citeulike-article-id = {3275546}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1119397}, journal = {Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1}, keywords = {bootstrapping, data-mining, evaluation, gazetteer, geographic}, pages = {18--25}, posted-at = {2008-09-16 20:26:10}, priority = {2}, publisher = {Association for Computational Linguistics}, title = {Semi-supervised learning of geographical gazetteers from the internet}, url = {http://portal.acm.org/citation.cfm?id=1119397}, year = {2003} }

TY - CONF ID - 5732 L3 - citeulike-article-id:3275546 N2 - In this paper we present an approach to the acquisition of geographical gazetteers. Instead of creating these resources manually, we propose to extract gazetteers from the World Wide Web, using Data Mining techniques.The bootstrapping approach, investigated in our study, allows us to create new gazetteers using only a small seed dataset (1260 words). In addition to gazetteers, the system produces classifiers. They can be used online to determine a class (CITY, ISLAND, RIVER, MOUNTAIN, REGION, COUNTRY) of any geographical name. Our classifiers perform with the average accuracy of 86.5%. JF - Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1 EP - 25 TI - Semi-supervised learning of geographical gazetteers from the internet PB - Association for Computational Linguistics SP - 18 KW - bootstrapping KW - data-mining KW - evaluation KW - gazetteer KW - geographic AU - Uryupina, Olga PY - 2003/// UR - http://portal.acm.org/citation.cfm?id=1119397 ER -