  Fine-grained entity typing (FET) aims to assign proper semantic types to
entity mentions according to their context, which is a fundamental task in
various entity-leveraging applications. Current FET systems usually establish
on large-scale weakly-supervised/distantly annotation data, which may contain
abundant noise and thus severely hinder the performance of the FET task.
Although previous studies have made great success in automatically identifying
the noisy labels in FET, they usually rely on some auxiliary resources which
may be unavailable in real-world applications (e.g. pre-defined hierarchical
type structures, human-annotated subsets). In this paper, we propose a novel
approach to automatically correct noisy labels for FET without external
resources. Specifically, it first identifies the potentially noisy labels by
estimating the posterior probability of a label being positive or negative
according to the logits output by the model, and then relabel candidate noisy
labels by training a robust model over the remaining clean labels. Experiments
on two popular benchmarks prove the effectiveness of our method. Our source
code can be obtained from https://github.com/CCIIPLab/DenoiseFET.
