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.
@article{arxiv.2305.13191,
title = {Taxonomy Expansion for Named Entity Recognition},
author = {Karthikeyan K and Yogarshi Vyas and Jie Ma and Giovanni Paolini and Neha Anna John and Shuai Wang and Yassine Benajiba and Vittorio Castelli and Dan Roth and Miguel Ballesteros},
journal= {arXiv preprint arXiv:2305.13191},
year = {2023}
}