Improving Distantly Supervised Relation Classification With Attention and Semantic Weight

Distantly supervised relation classification aims at identifying the relationship between two given entities and plays an essential part in natural language processing (NLP). Although distant supervision is able to generate labeled data automatically, it is facing with the problem of noisy data due to the wrong labeling problems. The attention mechanism is one of the most popular methods to reduce the influence of mislabeled data. However, regardless of the correlation among relations, the most existing methods treat all relationships as independent classes. In general, the definitions of relations contain rich semantic information, which improves the performance of the model, especially when classifying long-tail relations which lacks training data. Based on this idea, we propose a novel neural network architecture with an attention mechanism in this paper. First, we use bidirectional GRU to encode relation definitions as the context representations of relations. Then, we use the merge attention mechanism to make full use of the hidden states obtained by the GRU. To help the model make full use of the context of the entities, we also introduce semantic weights, calculated by the length of the shortest path between entities and words in the dependency tree. We conduct experiments on the widely used New York Times relation extraction corpus, and the results demonstrate that our model outperforms most of the state-of-the-art models.