Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity

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TypeArticle
Conference19th Canadian Conference on Artificial Intelligence, June 7, 2006., Québec City, Québec, Canada
AbstractIn this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as manually labeling training data or creating gazetteers. Second, the system can handle more than the three classical named-entity types (person, location, and organization). We describe the system's architecture and compare its performance with a supervised system. We experimentally evaluate the system on a standard corpus, with the three classical named-entity types, and also on a new corpus, with a new named-entity type (car brands).
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48727
NPARC number5763522
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Record identifier82701912-a9a3-4e63-a9ad-69279a1c30a5
Record created2009-03-29
Record modified2016-05-09
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