  Clinical named entity recognition (NER) aims to retrieve important entities
within clinical narratives. Recent works have demonstrated that large language
models (LLMs) can achieve strong performance in this task. While previous works
focus on proprietary LLMs, we investigate how open NER LLMs, trained
specifically for entity recognition, perform in clinical NER. In this paper, we
aim to improve them through a novel framework, entity decomposition with
filtering, or EDF. Our key idea is to decompose the entity recognition task
into several retrievals of sub-entity types. We also introduce a filtering
mechanism to remove incorrect entities. Our experimental results demonstrate
the efficacy of our framework across all metrics, models, datasets, and entity
types. Our analysis reveals that entity decomposition can recognize previously
missed entities with substantial improvement. We further provide a
comprehensive evaluation of our framework and an in-depth error analysis to
pave future works.
