Improving Named Entity Extraction Accuracy using Unlabeled Data and Several Extractors
Título del documento: Improving Named Entity Extraction Accuracy using Unlabeled Data and Several Extractors
Revista: Polibits
Base de datos: PERIÓDICA
Número de sistema: 000368145
ISSN: 1870-9044
Autores: 1
1
Instituciones: 1Fujitsu Laboratories Ltd, Kawasaki, Kanagawa. Japón
Año:
Periodo: Jul-Dic
Número: 40
País: México
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Experimental, aplicado
Resumen en inglés This paper proposes feature augmentation methods using unlabeled data and several Named Entity (NE) extractors. We collect NE–related information of each word (which we call NE–related labels) from unlabeled data by using NE extractors. NE–related labels which we collect include candidate NE class labels of each word and NE class labels of co–occurring words. To accurately collect the NE–related labels from unlabeled data, we consider methods to collect NE–related labels by using outputs of several NE extractors. We use NE–related labels as additional features for creating new NE extractors. We apply our NE extraction methods using the NE–related labels to IREX Japanese NE extraction task. The experimental results show better accuracy than the previous results obtained with NE extractors using handcrafted resources
Disciplinas: Ciencias de la computación
Palabras clave: Procesamiento de datos,
Recuperación de información,
Reconocimiento de lenguaje,
Extractores,
Datos no marcados
Keyword: Computer science,
Data processing,
Information retrieval,
Language recognition,
Extractors,
Unlabeled data
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