Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Using co-occurrence models for placename disambiguation
International Journal of Geographical Information Science
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
International Journal of Human-Computer Studies
A knowledge-based approach to named entity disambiguation in news articles
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Incorporating user feedback into name disambiguation of scientific cooperation network
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Heuristics- and statistics-based wikification
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Name disambiguation in scientific cooperation network by exploiting user feedback
Artificial Intelligence Review
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Precisely identifying entities is essential for semantic annotation. This paper addresses the problem of named entity disambiguation that aims at mapping entity mentions in a text onto the right entities in Wikipedia. The aim of this paper is to explore and evaluate various combinations of features extracted from Wikipedia and texts for the disambiguation task, based on a statistical ranking model of candidate entities. Through experiments, we show which combinations of features are the best choices for disambiguation.