A survey of named entity recognition and classification

• Autores: David Nadeau, Satoshi Sekine
• Localización: Linguisticae investigationes: Revue internationale de linguistique française et de linguistique générale, ISSN 0378-4169, Tome 30, Fascicule 1, 2007, págs. 3-26
• Idioma: inglés
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• Dialnet Métricas: 11 Citas
• Resumen

  • This survey covers fifteen years of research in the Named Entity Recognition and Classification (NERC) field, from 1991 to 2006. We report observations about languages, named entity types, domains and textual genres studied in the literature. From the start, NERC systems have been developed using hand-made rules, but now machine learning techniques are widely ued. These techniques are surveyed along with other critical aspects of NERC such as features and evaluation methods. Features are word-level, dictionary-level and corpus-level representations of words in a document. Evaluation techniques, ranging from intuitive exact match to very complex matching techniques with adjustable cost of erros, are an indisputable key to progress