  Training a Named Entity Recognition (NER) model often involves fixing a
taxonomy of entity types. However, requirements evolve and we might need the
NER model to recognize additional entity types. A simple approach is to
re-annotate entire dataset with both existing and additional entity types and
then train the model on the re-annotated dataset. However, this is an extremely
laborious task. To remedy this, we propose a novel approach called Partial
Label Model (PLM) that uses only partially annotated datasets. We experiment
with 6 diverse datasets and show that PLM consistently performs better than
most other approaches (0.5 - 2.5 F1), including in novel settings for taxonomy
expansion not considered in prior work. The gap between PLM and all other
approaches is especially large in settings where there is limited data
available for the additional entity types (as much as 11 F1), thus suggesting a
more cost effective approaches to taxonomy expansion.
