Daojian Zeng1,3, Yuan Dai1,3, Feng Li1,3, R. Simon Sherratt2, Jin Wang3,*
CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 121-136, 2018, DOI:10.3970/cmc.2018.055.121
Abstract Recently, many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction (DSRE). These approaches generally use a softmax classifier with cross-entropy loss, which inevitably brings the noise of artificial class NA into classification process. To address the shortcoming, the classifier with ranking loss is employed to DSRE. Uniformly randomly selecting a relation or heuristically selecting the highest score among all incorrect relations are two common methods for generating a negative class in the ranking loss function. However, the majority of the generated negative class can be easily discriminated from positive class and will contribute… More >
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