Describes an algorithm for improving the performance of unknown proper noun recognizers, using a statistical framework. We present a bootstrapping technique that starts out by using a training set to acquire contextual classification cues, and then uses the results of the initial phase to acquire additional training data from an unlabeled corpus. The training set (tagged proper nouns in contexts) is obtained trough an application of standard knowledge-based techniques for proper noun tagging, commonly used in information extraction systems.
A Statistical Technique for Bootstrapping Available Resources for Proper Nouns Classification / A., Cucchiarelli; Velardi, Paola. - (1999). (Intervento presentato al convegno International Conference on Information Intelligence and Systems tenutosi a Bethesda, MD, USA).
A Statistical Technique for Bootstrapping Available Resources for Proper Nouns Classification
VELARDI, Paola
1999
Abstract
Describes an algorithm for improving the performance of unknown proper noun recognizers, using a statistical framework. We present a bootstrapping technique that starts out by using a training set to acquire contextual classification cues, and then uses the results of the initial phase to acquire additional training data from an unlabeled corpus. The training set (tagged proper nouns in contexts) is obtained trough an application of standard knowledge-based techniques for proper noun tagging, commonly used in information extraction systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
