Prototypical networks relation classification model based on entity convolution

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Sentence-level relation classification is a technique for classifying the relation between the head entity and the tail entity in a sentence. Currently, it is popularly used to realize relation classification based on deep learning methods. However, these methods rely heavily on large-scale annotated data, and the role of head and tail entities’ information is not fully explored. In response to the above problems, we propose a prototypical networks model based on entity convolution for relation classification, which deforms the head entity and tail entity vectors encoded by BERT into multiple different convolution kernels and then performs convolution operations on the original sentence. Thus we can extract the features related to the entities in the sentence and classify the extracted features by using prototypical networks to realize relation classification. Experimental results strongly demonstrate that our model achieves state-of-the-art results compared with baseline models.