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From names to entities using thematic context distance
Abstract Name ambiguity arises from the polysemy ofName ambiguity arises from the polysemy of names and causes uncertainty about the true identity of entities referenced in unstructured text. This is a major problem in areas like information retrieval or knowledge management, for example when searching for a specific entity or updating an existing knowledge base. We approach this problem of named entity disambiguation (NED) using thematic information derived from Latent Dirichlet Allocation (LDA) to compare the entity mention's context with candidate entities in Wikipedia represented by their respective articles. We evaluate various distances over topic distributions in a supervised classification setting to find the best suited candidate entity, which is either covered in Wikipedia or unknown. We compare our approach to a state of the art method and show that it achieves significantly better results in predictive performance, regarding both entities covered in Wikipedia as well as uncovered entities. We show that our approach is in general language independent as we obtain equally good results for named entity disambiguation using the English, the German and the French Wikipedia.lish, the German and the French Wikipedia.
Abstractsub Name ambiguity arises from the polysemy ofName ambiguity arises from the polysemy of names and causes uncertainty about the true identity of entities referenced in unstructured text. This is a major problem in areas like information retrieval or knowledge management, for example when searching for a specific entity or updating an existing knowledge base. We approach this problem of named entity disambiguation (NED) using thematic information derived from Latent Dirichlet Allocation (LDA) to compare the entity mention's context with candidate entities in Wikipedia represented by their respective articles. We evaluate various distances over topic distributions in a supervised classification setting to find the best suited candidate entity, which is either covered in Wikipedia or unknown. We compare our approach to a state of the art method and show that it achieves significantly better results in predictive performance, regarding both entities covered in Wikipedia as well as uncovered entities. We show that our approach is in general language independent as we obtain equally good results for named entity disambiguation using the English, the German and the French Wikipedia.lish, the German and the French Wikipedia.
Bibtextype inproceedings  +
Doi 10.1145/2063576.2063700  +
Has author Pilz A. + , Paass G. +
Has extra keyword Knowledge base + , Latent dirichlet allocations + , Named entities + , Predictive performance + , State-of-the-art methods + , Supervised classification + , Thematic information + , True identity + , Wikipedia + , Classification (of information) + , Information retrieval + , Knowledge based systems + , Knowledge management + , Statistics + , Natural language processing systems +
Has keyword Classification + , Named entities + , Named Entity Disambiguation + , Named entity resolution + , Topic modeling +
Isbn 9781450307178  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 857–866  +
Published in International Conference on Information and Knowledge Management, Proceedings +
Title From names to entities using thematic context distance +
Type conference paper  +
Year 2011 +
Creation dateThis property is a special property in this wiki. 7 November 2014 19:38:12  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 19:38:12  +
DateThis property is a special property in this wiki. 2011  +
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