Efficient string matching: an aid to bibliographic search
Communications of the ACM
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Applying Semantic Social Graphs to Disambiguate Identity References
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
Context and Domain Knowledge Enhanced Entity Spotting in Informal Text
ISWC '09 Proceedings of the 8th International Semantic Web Conference
DBpedia spotlight: shedding light on the web of documents
Proceedings of the 7th International Conference on Semantic Systems
Ontology-driven automatic entity disambiguation in unstructured text
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
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Recently, named entity recognition tools tend to disambiguate recognized named entities on a very detailed level. Instead of elementary types (e.g. Person or Location), they assign concrete identifiers, trying to distinguish even different entities having same name and type (e.g. cities with the same name in different countries). We introduce a novel method for this kind of named entity disambiguation exploiting structural dependencies of recognized entities. We analyse the co-occurrence of disambiguated entities in the backing knowledge base and use this information to improve results of existing named entity disambiguation approaches. A model for co-occurrence representation is proposed and evaluated based on a dataset that we mine from Wikipedia.