Recent approaches in domain-specific named entity recognition (NER), such as
biomedical NER, have shown remarkable advances. However, they still lack of
faithfulness, producing erroneous predictions. We assume that knowledge of
entities can be useful in verifying the correctness of the predictions. Despite
the usefulness of knowledge, resolving such errors with knowledge is
nontrivial, since the knowledge itself does not directly indicate the
ground-truth label. To this end, we propose VerifiNER, a post-hoc verification
framework that identifies errors from existing NER methods using knowledge and
revises them into more faithful predictions. Our framework leverages the
reasoning abilities of large language models to adequately ground on knowledge
and the contextual information in the verification process. We validate
effectiveness of VerifiNER through extensive experiments on biomedical
datasets. The results suggest that VerifiNER can successfully verify errors
from existing models as a model-agnostic approach. Further analyses on
out-of-domain and low-resource settings show the usefulness of VerifiNER on
real-world applications.

最近在领域特定命名实体识别（NER）方面采取的方法取得了显著进展，但它们仍然缺乏准确性，产生错误预测。本文提出了一种后续验证框架 VerifiNER，通过利用知识来从现有的 NER 方法中识别错误并修正成更为准确的预测。我们的框架利用大型语言模型的推理能力和背景信息，在验证过程中充分基于知识。通过对生物医学数据集进行广泛实验验证了 VerifiNER 的有效性。结果表明，作为一种模型无关的方法，VerifiNER 可以成功验证现有模型的错误。对于领域外和资源有限情况的进一步分析显示了 VerifiNER 在实际应用中的有用性。