Web-a-where: geotagging web content
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Location normalization for information extraction
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Grounding spatial named entities for information extraction and question answering
HLT-NAACL-GEOREF '03 Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1
HLT-NAACL-GEOREF '03 Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1
Geographic reference analysis for geographic document querying
HLT-NAACL-GEOREF '03 Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1
Named Entity Disambiguation: A Hybrid Statistical and Rule-Based Incremental Approach
ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
Towards Click-Based Models of Geographic Interests in Web Search
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Do We Need Entity-Centric Knowledge Bases for Entity Disambiguation?
Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies
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Knowledge rich approach of processing documents has been viewed as a method to improve over simple bag-of- word representation. Extracting location information from documents and link them to some ontology such as world gazetteer through a disambiguation process becomes an interesting and important topic. Lacking of training data is a problem in disambiguation method. In this paper we described a method to automatically extract training data from large collection of documents based on local context disambiguation, and then sense profiles are generated automatically for disambiguation use. Another topic of this paper is to describe a linear combination method to combine different types of evidences of disambiguation. We explored three different evidences including location sense context in training documents, local neighbor context, and the popularity of individual location sense. Our results show that combining the three evidences generates reasonable results.