Low-resource Complex Named Entity Recognition aims to detect entities with the form of any
linguistic constituent under scenarios with limited manually annotated data. Existing studies …

Abstract Training Named Entity Recognition (NER) models typically necessitates the use of
extensively annotated datasets. This requirement presents a significant challenge due to the …

Data augmentation techniques have been used to improve the generalization capability of
models in the named entity recognition (NER) tasks. Existing augmentation methods either …

Data augmentation is an important technique for enhancing machine learning performance.
In this study, we propose a novel generative data augmentation method for named entity …

Data augmentation is an effective solution to improve model performance and robustness for
low-resource named entity recognition (NER). However, synthetic data often suffer from poor …

Recent advances in deep learning have brought remarkable performance improvements in
named entity recognition (NER), specifically in token-level classification problems. However …

Complex Named Entity Recognition (NER) is the task of detecting linguistically complex
named entities in low-context text. In this paper, we present ACLM Attention-map aware …

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 …

Cross-lingual Named Entity Recognition (NER) aims to address the challenge of data
scarcity in low-resource languages by leveraging knowledge from high-resource languages …

Data augmentation techniques have been used to alleviate the problem of scarce labeled
data in various NER tasks (flat, nested, and discontinuous NER tasks). Existing …