  Named entity recognition on the in-domain supervised and few-shot settings
have been extensively discussed in the NLP community and made significant
progress. However, cross-domain NER, a more common task in practical scenarios,
still poses a challenge for most NER methods. Previous research efforts in that
area primarily focus on knowledge transfer such as correlate label information
from source to target domains but few works pay attention to the problem of
label conflict. In this study, we introduce a label alignment and reassignment
approach, namely LAR, to address this issue for enhanced cross-domain named
entity recognition, which includes two core procedures: label alignment between
source and target domains and label reassignment for type inference. The
process of label reassignment can significantly be enhanced by integrating with
an advanced large-scale language model such as ChatGPT. We conduct an extensive
range of experiments on NER datasets involving both supervised and zero-shot
scenarios. Empirical experimental results demonstrate the validation of our
method with remarkable performance under the supervised and zero-shot
out-of-domain settings compared to SOTA methods.
