论文标题
ULF：使用交叉验证进行弱监督的无监督标签函数校正
ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision
论文作者
论文摘要
手动数据标记的一种具有成本效益的替代方案是弱监督（WS），其中使用预定义的标签功能（LFS）自动注释数据样本，即基于规则的机制，这些机制生成了相关类的人工标记。在这项工作中，我们根据K折交叉验证原理研究WS的降噪技术。我们引入了一种新的算法ULF，以进行无监督的标记功能校正，该校正校正可以通过利用对所有LFS以外的所有LFS培训的模型来识别和纠正固定LFS特定的偏见，从而确定WS数据。具体而言，ULF通过在高度可靠的交叉验证样品上重新估计该分配来完善LFS对类别的分配。多个数据集的评估证实了ULF在增强WS学习的有效性的情况下，而无需手动标记。
A cost-effective alternative to manual data labeling is weak supervision (WS), where data samples are automatically annotated using a predefined set of labeling functions (LFs), rule-based mechanisms that generate artificial labels for the associated classes. In this work, we investigate noise reduction techniques for WS based on the principle of k-fold cross-validation. We introduce a new algorithm ULF for Unsupervised Labeling Function correction, which denoises WS data by leveraging models trained on all but some LFs to identify and correct biases specific to the held-out LFs. Specifically, ULF refines the allocation of LFs to classes by re-estimating this assignment on highly reliable cross-validated samples. Evaluation on multiple datasets confirms ULF's effectiveness in enhancing WS learning without the need for manual labeling.
