Recently, many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction (DSRE). However, these approaches generally employ a softmax classifier with cross-entropy loss, and bring the noise of artificial class NA into classification process. Moreover, the class imbalance problem is serious in the automatically labeled data, and results in poor classification rates on minor classes in traditional approaches. In this work, we exploit cost-sensitive ranking loss to improve DSRE. It first uses a Piecewise Convolutional Neural Network (PCNN) to embed the semantics of sentences. Then the features are fed into a classifier which takes into account both the ranking loss and cost-sensitive. Experiments show that our method is effective and performs better than state-of-the-art methods.
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