Effect of depth order on iterative nested named entity recognition models

TL;DR

This paper introduces an order-agnostic iterative nested NER model with a modified Transformer architecture, demonstrating that processing entities from smallest to largest yields the best performance in biomedical text extraction.

Contribution

The paper proposes a novel order-agnostic iterative nested NER model and a method to select optimal entity processing order, improving nested entity recognition performance.

Findings

Smallest to largest order yields best results.

Order-agnostic model adapts to different entity hierarchies.

Modified Transformer effectively incorporates previous predictions.

Abstract

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.