How well, then, could the standard+g model do if it could somehow reduce the number of errors in the top-right cell?
in recent years discriminative probabilistic models have been successfully applied to a number of information extraction tasks in natural language processing (NLP), such as named entity recognition (NER) (McCallum and Li, 2003), noun phrase chunking (Sha and Pereira, 2003) and information extraction from research papers (Peng and McCallum, 2004).