Cross-domain named entity recognition (NER), aiming to address the limitation of labeled
resources in the target domain, is a challenging yet important task. Most existing studies …

Large language models (LLMs) effectively generate fluent text when the target output follows
natural language patterns. However, structured prediction tasks confine the output format to …

Cross-domain NER is a challenging task to address the low-resource problem in practical
scenarios. Previous typical solutions mainly obtain a NER model by pre-trained language …

To address the scarcity of massive labeled data, cross-domain named entity recognition
(cross-domain NER) attracts increasing attention. Recent studies focus on decomposing …

Recent studies have shown remarkable success in cross-domain named entity recognition
(cross-domain NER). Despite the promising results, existing methods mainly utilize pre …

Benefiting many information retrieval applications, named entity recognition (NER) has
shown impressive progress. Recently, there has been a growing trend to decompose …

Cross-domain named entity recognition (NER) tasks encourage NER models to transfer
knowledge from data-rich source domains to sparsely labeled target domains. Previous …

The evolution of communication technology has driven the demand for intelligent power
grids and data analysis in power systems. However, obtaining and annotating electrical data …

Few-shot named entity recognition (NER) has shown remarkable progress in identifying
entities in low-resource domains. However, few-shot NER methods still struggle with out-of …

Abstract Cross-domain Named Entity Recognition (CDNER) is crucial for Knowledge Graph
(KG) construction and natural language processing (NLP), enabling learning from source to …