Our original goal was to adapt TBL for error correction of high-performing models (Wu et al., 2004a), with two main principles: (1) since it is not clear that the usual assumptions made about the distribution of the training/test data are valid in such extreme operating ranges, empirical observations would take precedence over theoretical models, which implies that (2) any model would have to be empirically justified by testing on a diverse range of data.
the experiments run in this section were performed on the data sets used in the CoNLL-2002 andCoNLL-2003 Named Entity Recognition shared tasks.