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Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity

Nadeau, David and Turney, Peter D. and Matwin, Stan (2006) Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity. [Conference Poster]

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Abstract

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).

Item Type:Conference Poster
Keywords:named entity, unsupervised named entity recognition
Subjects:Computer Science > Language
Computer Science > Machine Learning
Computer Science > Artificial Intelligence
ID Code:5025
Deposited By: Nadeau, David
Deposited On:01 Aug 2006
Last Modified:11 Mar 2011 08:56

References in Article

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