  This paper studies the effect of the order of depth of mention on nested
named entity recognition (NER) models. NER is an essential task in the
extraction of biomedical information, and nested entities are common since
medical concepts can assemble to form larger entities. Conventional NER systems
only predict disjointed entities. Thus, iterative models for nested NER use
multiple predictions to enumerate all entities, imposing a predefined order
from largest to smallest or smallest to largest. We design an order-agnostic
iterative model and a procedure to choose a custom order during training and
prediction. To accommodate for this task, we propose a modification of the
Transformer architecture to take into account the entities predicted in the
previous steps. We provide a set of experiments to study the model's
capabilities and the effects of the order on performance. Finally, we show that
the smallest to largest order gives the best results.
