Vincenzo Moscato, Marco Postiglione, Giancarlo Sperlì, Andrea Vignali
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引用次数: 0
摘要
训练命名实体识别(NER)模型通常需要使用大量注释数据集。由于人工标注劳动密集且成本高昂,尤其是在医学和金融等专业领域,这一要求带来了巨大的挑战。为了解决数据稀缺的问题,有两种有效的策略:(1) 主动学习(Active Learning,AL),它能自动识别如果注释后最能提高模型性能的样本;(2) 数据增强(data augmentation,自动生成新样本)。然而,虽然主动学习可以减少人工操作,但并不能完全消除人工操作,而且数据扩增往往会导致注释不完整和有噪声,给 NER 模型训练带来新的障碍。在本研究中,我们将 AL 原则整合到数据扩增框架中,命名为基于主动学习的 NER 数据扩增(ALDANER),以便优先从扩增池中选择信息样本,并减轻噪声注释的影响。我们在各种基准数据集和少数几个场景中进行的实验表明,我们的方法超越了几种数据扩增基线,为未来的研究提供了有前途的途径。
ALDANER: Active Learning based Data Augmentation for Named Entity Recognition
Training Named Entity Recognition (NER) models typically necessitates the use of extensively annotated datasets. This requirement presents a significant challenge due to the labor-intensive and costly nature of manual annotation, especially in specialized domains such as medicine and finance. To address data scarcity, two strategies have emerged as effective: (1) Active Learning (AL), which autonomously identifies samples that would most enhance model performance if annotated, and (2) data augmentation, which automatically generates new samples. However, while AL reduces human effort, it does not eliminate it entirely, and data augmentation often leads to incomplete and noisy annotations, presenting new hurdles in NER model training. In this study, we integrate AL principles into a data augmentation framework, named Active Learning-based Data Augmentation for NER (ALDANER), to prioritize the selection of informative samples from an augmented pool and mitigate the impact of noisy annotations. Our experiments across various benchmark datasets and few-shot scenarios demonstrate that our approach surpasses several data augmentation baselines, offering insights into promising avenues for future research.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.