AI 中文总结
该研究针对小数据集下多领域NER的挑战，采用无监督预训练结合迁移学习，整合数据增强等技术以提升NER系统在资源受限领域的性能与泛化能力。
AI 中文摘要
本文探讨了在下游信息抽取任务（多领域命名实体识别（NER））中，利用未标注小数据集或有限数据集学习高质量表示所面临的挑战与相关方法。传统NER系统通常依赖大量标注数据，这对许多领域而言并不现实，因此本研究采用无监督预训练方法，在无标注数据集的前提下预训练并识别实体，随后将迁移学习模型应用于不同模拟有限数据集的NER任务。NER在自然语言处理（NLP）中至关重要，可识别并分类文本中的相关实体。本研究针对领域差异性、数据稀疏性与过拟合问题，研究了数据增强、少样本学习、领域对抗训练等创新方法，整合这些技术有望提升NER系统在多样化且资源受限领域的性能与泛化能力，为更高效、适配性更强的NLP应用奠定基础。
英文摘要
This paper explores the challenges and the methodologies associated with learning quality representations in scenarios with unlabelled small or limited datasets for downstream information extraction task (Multidomain Named Entity Recognition (NER). The study adopts a Transfer Learning on small datasets. Traditional NER systems often rely on large, labelled data, which is impractical for many domains. This study, therefore, applies an unsupervised pre-training approach to precondition and identify entities without annotated datasets, then applies transfer learning models to different simulated limited datasets for a named entity recognition task. Entity Recognition (NER) is essential in natural language processing (NLP), it identifies and classifies related entities within the text. This study addresses the complexities of domain variability, data sparsity, and overfitting and investigates innovative approaches such as data augmentation, few-shot learning, and domain adversarial training. Integrating these techniques promises to enhance the performance and generalizability of NER systems across diverse and resource-constrained domains, paving the way for more efficient and adaptable NLP applications.
