{"_id":"MMfyx26iThfqW3C2B","bibbaseid":"blessing-schutze-finegrainedgeographicalrelationextractionfromwikipedia-2006","authorIDs":[],"author_short":["Blessing, A.","Schutze, H."],"bibdata":{"title":"Fine-Grained Geographical Relation Extraction from Wikipedia","type":"article","year":"2006","pages":"2949-2952","websites":"http://www.lrec-conf.org/proceedings/lrec2010/pdf/519_Paper.pdf","id":"d1f4f16c-9526-3d07-931a-ff5ddb6f60a1","created":"2011-03-13T12:54:42.000Z","file_attached":false,"profile_id":"5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6","group_id":"066b42c8-f712-3fc3-abb2-225c158d2704","last_modified":"2017-03-14T14:36:19.698Z","tags":"relation extraction","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Blessing2006","private_publication":false,"abstract":"In this paper, we present work on enhancing the basic data resource of a context-aware system. First, we introduce a supervised approach to extracting geographical relations on a fine-grained level. Second, we present a novel way of using Wikipedia as a corpus based on self-annotation. A self-annotation is an automatically created high-quality annotation that can be used for training and evaluation. The fined-grained relations are used to complete gazetteer data. The precision and recall scores of more than 97% confirm that a statistical IE pipeline can be used to improve the data quality of community-based resources.","bibtype":"article","author":"Blessing, Andre and Schutze, H","journal":"lrecconforg","bibtex":"@article{\n title = {Fine-Grained Geographical Relation Extraction from Wikipedia},\n type = {article},\n year = {2006},\n pages = {2949-2952},\n websites = {http://www.lrec-conf.org/proceedings/lrec2010/pdf/519_Paper.pdf},\n id = {d1f4f16c-9526-3d07-931a-ff5ddb6f60a1},\n created = {2011-03-13T12:54:42.000Z},\n file_attached = {false},\n profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},\n group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},\n last_modified = {2017-03-14T14:36:19.698Z},\n tags = {relation extraction},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Blessing2006},\n private_publication = {false},\n abstract = {In this paper, we present work on enhancing the basic data resource of a context-aware system. First, we introduce a supervised approach to extracting geographical relations on a fine-grained level. Second, we present a novel way of using Wikipedia as a corpus based on self-annotation. A self-annotation is an automatically created high-quality annotation that can be used for training and evaluation. The fined-grained relations are used to complete gazetteer data. The precision and recall scores of more than 97% confirm that a statistical IE pipeline can be used to improve the data quality of community-based resources.},\n bibtype = {article},\n author = {Blessing, Andre and Schutze, H},\n journal = {lrecconforg}\n}","author_short":["Blessing, A.","Schutze, H."],"urls":{"Website":"http://www.lrec-conf.org/proceedings/lrec2010/pdf/519_Paper.pdf"},"bibbaseid":"blessing-schutze-finegrainedgeographicalrelationextractionfromwikipedia-2006","role":"author","downloads":0,"html":""},"bibtype":"article","creationDate":"2020-02-06T23:48:11.761Z","downloads":0,"keywords":[],"search_terms":["fine","grained","geographical","relation","extraction","wikipedia","blessing","schutze"],"title":"Fine-Grained Geographical Relation Extraction from Wikipedia","year":2006}