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TY - GEN ID - cogprints5025 UR - http://cogprints.org/5025/ A1 - Nadeau, David A1 - Turney, Peter D. A1 - Matwin, Stan Y1 - 2006/// N2 - In 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). KW - named entity KW - unsupervised named entity recognition TI - Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity SP - 266 AV - public EP - 277 ER -