Named entity recognition (NER) is a fundamental part of extracting
information from documents in biomedical applications. A notable advantage of
NER is its consistency in extracting biomedical entities in a document context.
Although existing document NER models show consistent predictions, they still
do not meet our expectations. We investigated whether the adjectives and
prepositions within an entity cause a low label consistency, which results in
inconsistent predictions. In this paper, we present our method, ConNER, which
enhances the label dependency of modifiers (e.g., adjectives and prepositions)
to achieve higher label agreement. ConNER refines the draft labels of the
modifiers to improve the output representations of biomedical entities. The
effectiveness of our method is demonstrated on four popular biomedical NER
datasets; in particular, its efficacy is proved on two datasets with 7.5-8.6%
absolute improvements in the F1 score. We interpret that our ConNER method is
effective on datasets that have intrinsically low label consistency. In the
qualitative analysis, we demonstrate how our approach makes the NER model
generate consistent predictions. Our code and resources are available at
this https URL.

本文介绍了一种名为 ConNER 的方法，通过提高修饰词（如形容词和介词）的标签依赖性来实现更高的标签一致性，从而改善生物医学实体的输出表示。该方法在四个流行的生物医学 NER 数据集上证明了其有效性，并在两个数据集上实现了 7.5-8.6％ 的绝对改进。