揭示并解决远程标注中的隐性噪声问题，提升命名实体识别效果
Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations
- 区分未标注实体与噪声实体两类问题，提出针对性解决方案
- 在8个真实数据集上超越现有最优方法，验证了有效性
- 适用于规则与大模型监督等多种远程标注场景
远程监督命名实体识别（DS-NER）已成为替代人工标注的低成本方案，通过文本与外部资源对齐自动构建训练数据。尽管已有大量噪声评估工作，但很少关注不同远程标注方法间的隐性噪声分布。本文从两个方面探索DS-NER的有效性与鲁棒性：(1) 远程标注技术，涵盖传统基于规则的方法与新兴的大语言模型监督方法；(2) 噪声评估，提出一种新框架，将问题明确划分为未标注实体问题（UEP）和噪声实体问题（NEP），并为每类提供专用解决方案。所提方法在来自三个数据源、采用四种不同标注技术的八个真实世界数据集上实现显著提升，证明其优于当前最先进方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。