迈向DS-NER：揭示并解决远程标注中的潜在噪声
Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations
AI总结：
本研究聚焦远程监督命名实体识别中不同标注方法间的潜在噪声问题，提出将噪声分为未标注实体问题和噪声实体问题的新评估框架及对应方案，在多源多技术数据集上优于现有最优方法。
摘要：
远程监督命名实体识别（DS-NER）已成为传统人工标注方法的一种低成本便捷替代方案，它通过将文本与外部资源对齐来自动生成训练数据。尽管现有研究在噪声测量方法上付出了诸多努力，但很少有工作聚焦于不同远程标注方法之间的潜在噪声分布。在本研究中，我们从两个方面探究DS-NER的有效性与鲁棒性：（1）远程标注技术，既涵盖传统的基于规则的方法，也包括创新的大语言模型监督方法；（2）噪声评估，为此我们提出了一个全新框架。该框架将面临的挑战明确划分为未标注实体问题（UEP）和噪声实体问题（NEP），随后针对每类问题提供了专门的解决方案。我们提出的方法在8个真实世界远程监督数据集上取得了显著提升，这些数据集来自3个不同数据源，涉及4种不同的标注技术，证实了其优于当前最先进方法的性能。
英文摘要：
Distantly supervised named entity recognition (DS-NER) has emerged as a cheap and convenient alternative to traditional human annotation methods, enabling the automatic generation of training data by aligning text with external resources. Despite the many efforts in noise measurement methods, few works focus on the latent noise distribution between different distant annotation methods. In this work, we explore the effectiveness and robustness of DS-NER by two aspects: (1) distant annotation techniques, which encompasses both traditional rule-based methods and the innovative large language model supervision approach, and (2) noise assessment, for which we introduce a novel framework. This framework addresses the challenges by distinctly categorizing them into the unlabeled-entity problem (UEP) and the noisy-entity problem (NEP), subsequently providing specialized solutions for each. Our proposed method achieves significant improvements on eight real-world distant supervision datasets originating from three different data sources and involving four distinct annotation techniques, confirming its superiority over current state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
