融合门控机制的远程监督关系抽取方法

Distant Supervision for Relation Extraction with Gate Mechanism

提出一种融合门控机制的远程监督关系抽取方法。首先在词级别上自动选择正相关特征,过滤与关系标签无关的词级别噪声;然后在门控机制内引入软标签的思想,弱化硬标签对噪声过滤的影响;最后结合句子级别的噪声过滤,提升模型的整体性能。在公开数据集上的实验结果表明,相对于句子级别噪声过滤方法,所提方法的性能有显著提高。

A piecewise convolutional neural network with gating mechanism is proposed,which would automatically filter positive correlation features at word-level.Moreover,the idea of soft-label is introduced to the gating mechanism to weaken the impact of hard labels on noise filtering.Combined with sentence-level noise filtering,the overall performance of the model is improved.The experimental results on the public dataset show that the proposed model has a significant improvement compared to the sentence-level noise filtering methods.