基于PCNN相似句袋注意力的远程监督关系抽取方法

Distant Supervision Relation Extraction Based on PCNN Similar Bag Attention

在关系抽取任务中,远程监督通过对齐知识库(KB)和文本来自动生成训练数据,从而解决了人工标注数据的问题。然而,远程监督不可避免会伴随着错误标签的问题。为了解决错误标签的问题,该文提出了基于PCNN(分段卷积神经网络)相似句袋注意力的远程监督关系抽取方法(PCNN-PATT-SBA),该模型提出了基于高斯分布的位置注意力机制(PATT),通过对非实体词与实体词之间的位置关系建模,为句子中每个单词分配相应的权重,从而降低噪声词的影响。另外,基于不同句袋之间的特征相似性,该文提出了相似句袋注意力机制(SBA),通过融合相似句袋的特征,从而达到解决单句子句袋信息过少的问题。在数据集New York Times(NYT)上的实验结果证明了该文提出方法的有效性,并且相对于句袋间注意力模型,在P@N值上提高了6.9%。

In the task of relation extraction,distant supervision automatically generates training data by aligning the knowledge base(KB)and text,thereby solving the problem of manual data annotation.However,distant supervision will inevitably be accompanied by the problem of wrong labeling.In order to solve the problem of wrong labeling,this paper proposes a distant supervision relation extraction method based on PCNN(piecewise convolutional neural networks)similar sentence bag attention(PCNN-PATT-SBA).This model proposes the position attention mechanism(PATT)based on Gaussian distribution,which models the position relationship between non-entity words and entity words,assigns corresponding weights to each word in the sentence,thereby reducing the influence of noise words.In addition,based on the feature similarity between bag-of-sentences,this paper proposes the similar sentence bag attention mechanism(SBA),which can enrich a single sentence bag by merging the characteristics of similar bags.The experimental results on the dataset New York Times(NYT)prove the proposed method increases the P@N by 6.9%compared to the inter-cross-bag model.