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.

翻译：近期在特定领域命名实体识别（NER）方法（如生物医学NER）取得了显著进展，但仍存在忠实性不足的问题，易产生错误预测。我们认为实体知识有助于验证预测结果的正确性。尽管知识具有实用价值，但利用知识解决此类错误并非易事，因为知识本身并未直接指示真实标签。为此，我们提出VerifiNER——一种事后验证框架，通过知识识别现有NER方法的错误并将其修正为更可靠的预测。该框架利用大语言模型的推理能力，在验证过程中充分结合知识依赖与上下文信息。通过生物医学数据集上的大量实验，我们验证了VerifiNER的有效性。结果表明，VerifiNER作为一种模型无关方法，能够成功检测现有模型的错误。进一步针对跨领域和低资源场景的分析，展示了VerifiNER在实际应用中的实用价值。