| Summary: | Named entity recognition has achieved remarkable success on benchmarks with high-quality manual annotations. Such annotations are labor-intensive and time-consuming, thus unavailable in real-world scenarios. An emerging interest is to generate low-cost but noisy labels via distant supervision, hence noisy label learning algorithms are in demand. In this paper, a unified self-adaptive learning framework termed Self-Adaptive Label cOrrection (SALO) is proposed. SALO adaptively performs a label correction process, both in an implicit and an explicit manners, turning noisy labels into correct ones, thus benefiting model training. The experimental results on four benchmark datasets demonstrated the superiority of SALO over the state-of-the-art distantly supervised methods. Moreover, a better version of noisy labels by ensembling several semantic matching methods was built. Experiments were carried out and consistent improvements were observed, validating the generalization of the proposed SALO.
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