One Model to Recognize Them All:Marginal Distillation from NER Models with Different Tag Sets
Table 3: Precision, Recall, and F1 score results for full tag set integration experiments. The best results are in bold.
['System', 'OntoNotes 5.0 Prec', 'OntoNotes 5.0 Rec', 'OntoNotes 5.0 F1', 'I2B2 2014 Prec', 'I2B2 2014 Rec', 'I2B2 2014 F1']
As shown, both Mardi and Mardi-Data achieve similar results as Marginal CRF in the full tag set integration setting. Again, joint-training-based methods significantly outperform the Post Processing baseline for both BiLSTM-based and BiLSTM-CRF-based systems. The results show that Mardi can principally integrate NER models trained with heterogeneous tag sets using an easy-to-build tag hierarchy.