  The noisy labeling problem has been one of the major obstacles for distant
supervised relation extraction. Existing approaches usually consider that the
noisy sentences are useless and will harm the model's performance. Therefore,
they mainly alleviate this problem by reducing the influence of noisy
sentences, such as applying bag-level selective attention or removing noisy
sentences from sentence-bags. However, the underlying cause of the noisy
labeling problem is not the lack of useful information, but the missing
relation labels. Intuitively, if we can allocate credible labels for noisy
sentences, they will be transformed into useful training data and benefit the
model's performance. Thus, in this paper, we propose a novel method for distant
supervised relation extraction, which employs unsupervised deep clustering to
generate reliable labels for noisy sentences. Specifically, our model contains
three modules: a sentence encoder, a noise detector and a label generator. The
sentence encoder is used to obtain feature representations. The noise detector
detects noisy sentences from sentence-bags, and the label generator produces
high-confidence relation labels for noisy sentences. Extensive experimental
results demonstrate that our model outperforms the state-of-the-art baselines
on a popular benchmark dataset, and can indeed alleviate the noisy labeling
problem.
