Label Alignment and Reassignment with Generalist Large Language Model for Enhanced Cross-Domain Named Entity Recognition
This addresses a practical problem in NLP for cross-domain NER by focusing on label conflict, which is incremental as it builds on prior knowledge transfer methods.
The paper tackles label conflict in cross-domain named entity recognition by introducing a label alignment and reassignment approach, achieving remarkable performance improvements in supervised and zero-shot out-of-domain settings compared to state-of-the-art methods.
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.