Scaling question answering to the Web
Proceedings of the 10th international conference on World Wide Web
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Web-scale information extraction in knowitall: (preliminary results)
Proceedings of the 13th international conference on World Wide Web
Fine grained classification of named entities
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Entity categorization over large document collections
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Open information extraction from the web
Communications of the ACM - Surviving the data deluge
Fine-grained classification of named entities exploiting latent semantic kernels
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Instance-based ontology population exploiting named-entity substitution
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Leveraging community-built knowledge for type coercion in question answering
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part II
Named entity recognition and disambiguation using linked data and graph-based centrality scoring
SWIM '12 Proceedings of the 4th International Workshop on Semantic Web Information Management
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Domain-specific semantic relatedness from Wikipedia: can a course be transferred?
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
Entity discovery and annotation in tables
Proceedings of the 16th International Conference on Extending Database Technology
Structured data and inference in DeepQA
IBM Journal of Research and Development
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Many applications make use of named entity classification. Machine learning is the preferred technique adopted for many named entity classification methods where the choice of features is critical to final performance. Existing approaches explore only the features derived from the characteristic of the named entity itself or its linguistic context. With the development of the Semantic Web, a large number of data sources are published and connected across the Web as Linked Open Data (LOD). LOD provides rich a priori knowledge about entity type information, knowledge that can be a valuable asset when used in connection with named entity classification. In this paper, we explore the use of LOD to enhance named entity classification. Our method extracts information from LOD and builds a type knowledge base which is used to score a (named entity string, type) pair. This score is then injected as one or more features into the existing classifier in order to improve its performance. We conducted a thorough experimental study and report the results, which confirm the effectiveness of our proposed method.