基于GRU和注意力机制的远程监督关系抽取

With the development of deep learning, more and more deep learning models have been applied to the task of relation extraction, but traditional deep learning models cannot solve long distance dependence problems. At the same time, distant supervision will inevitably generate wrong labels. For these two problems, this work proposes a distant supervision relationship extraction method based on GRU (Gated Recurrent Unit) and the attention mechanism. First, the GRU neural network is adopted to extract text features and solve long-distance dependence problems. Second this work constructs a Sentence-Level Attention Mechanism on entity pairs to reduce the weight of noise sentences. Finally, based on the real data set, by calculating the accuracy rate and recall rate, the PR curve is drawn to prove the proposed method has achieved significant progress compared with some existing methods.