Named Entity Recognition (NER) in low-resource settings aims to identify and categorize
entities in a sentence with limited labeled data. Although prompt-based methods have …

Abstract Traditional Named Entity Recognition (NER) models are typically designed for
domain-specific datasets and limited to fixed predefined types, resulting in difficulty …

While the abundance of rich and vast datasets across numerous fields has facilitated the
advancement of natural language processing, sectors in need of specialized data types …

Abstract Most existing Named Entity Recognition (NER) methods require a large scale of
labeled data and exhibit poor performance in low-resource scenarios. Thus in this paper, we …

Named entity recognition (NER) is a fundamental information extraction task that seeks to
identify entity mentions of certain types in text. Despite numerous advances, the existing …

Pre-trained Language Models (PLMs) have been applied in NLP tasks and achieve
promising results. Nevertheless, the fine-tuning procedure needs labeled data of the target …

Deep neural models for named entity recognition (NER) have shown impressive results in
overcoming label scarcity and generalizing to unseen entities by leveraging distant …

Named Entity Recognition (NER) is an essential steppingstone in the field of natural
language processing. Although promising performance has been achieved by various …

Recently, it has attracted much attention to build reliable named entity recognition (NER)
systems using limited annotated data. Nearly all existing works heavily rely on domain …

Abstract Named Entity Recognition (NER) in lowresource languages has been a long-
standing challenge in NLP. Recent work has shown great progress in two directions …