Search

Distant supervision (DS) can automatically generate annotated data for relation extraction (RE) with knowledge bases and corpora. The existing DS methods that train on bags selected by attention mechanism are susceptible to noisy bags and neglect useful information in noisy bags. In this paper, we propose DCSR, a novel DS method which utilizes deep clustering to obtain refined superbag representations for solving the wrong labeling problem. we substitute deep clustering for selective attention to construct superbags, capturing helpful information between spatially-close bags, including noisy bags. Moreover, we implement data augmentation on the input sentences to handle the long-tail problem. Experiments on the NYT2010 and NYT-H datasets show that our method can effectively improve RE and significantly outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]