Effect of depth order on iterative nested named entity recognition models
It addresses nested entity recognition in biomedical text, an incremental improvement over existing iterative methods.
This paper investigates how the order of processing nested entities affects iterative named entity recognition models, finding that processing from smallest to largest yields the best performance.
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.