  This paper delves into Named Entity Recognition (NER) under the framework of
Distant Supervision (DS-NER), where the main challenge lies in the compromised
quality of labels due to inherent errors such as false positives, false
negatives, and positive type errors. We critically assess the efficacy of
current DS-NER methodologies using a real-world benchmark dataset named QTL,
revealing that their performance often does not meet expectations. To tackle
the prevalent issue of label noise, we introduce a simple yet effective
approach, Curriculum-based Positive-Unlabeled Learning CuPUL, which
strategically starts on "easy" and cleaner samples during the training process
to enhance model resilience to noisy samples. Our empirical results highlight
the capability of CuPUL to significantly reduce the impact of noisy labels and
outperform existing methods. QTL dataset and our code is available on GitHub.
