基于GRU和注意力机制的远程监督关系抽取

随着深度学习的发展，越来越多的深度学习模型被运用到了关系提取的任务中，但是传统的深度学习模型无法解决长距离依赖问题；同时，远程监督将会不可避免地产生错误标签。针对以上两个问题，提出一种基于GRU（gated recurrent unit）和注意力机制的远程监督关系抽取方法，首先通过使用GRU神经网络来提取文本特征，解决长距离依赖问题；接着在实体对上构建句子级的注意力机制，减小噪声句子的权重；最后在真实的数据集上，通过计算准确率、召回率并绘出PR曲线证明该方法与现有的一些方法相比，取得了比较显著的进步。

Distant supervision relationship extraction based on GRU and attention mechanism

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 cann''t solve long distance dependence problems. At the same time, distant supervision will inevitably generate wrong labels. For these two problems, this paper proposed a distant supervision relationship extraction method based on GRU(gated recurrent unit) and the attention mechanism. First, it adopted the GRU neural network to extract text features and solve long-distance dependence problems. Second it constructed 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, and drawing the PR curve to prove the proposed method has achieved significant progress compared with some existing methods.