Paper: Empirical Study On The Performance Stability Of Named Entity Recognition Model Across Domains

ACL ID W06-1660
Title Empirical Study On The Performance Stability Of Named Entity Recognition Model Across Domains
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

When a machine learning-based named entity recognition system is employed in a new domain, its performance usually de- grades. In this paper, we provide an em- pirical study on the impact of training data size and domain information on the per- formance stability of named entity recog- nition models. We present an informative sample selection method for building high quality and stable named entity recogni- tion models across domains. Experimen- tal results show that the performance of the named entity recognition model is en- hanced significantly after being trained with these informative samples.