基于卷积神经网络的中文医疗弱监督关系抽取

随着医疗领域受到越来越多的关注,自然语言处理的理论和应用逐渐拓展到该领域,其中信息抽取技术在该领域的应用成为研究热点。针对信息抽取技术在医疗领域实体关系抽取中的应用,提出一种基于卷积神经网络的弱监督关系抽取方法。该方法通过添加人工规则使训练语料带有实体关系标签,然后将该弱关系训练语料转换为向量特征矩阵,并输入到卷积神经网络进行分类模型训练,最终实现实体关系抽取。实验结果表明,该方法比常规机器学习方法更加准确高效。

Chinese Medical Weak Supervised Relation Extraction Based on Convolution Neural Network

With medical field are receiving more and more attention,the theory and application of natural language processing began to expand the field,and information extraction technology in the field of application has become a research hotspot.In this paper,based on the application of information extraction technology in medical domain entity relation extraction,a weak supervised relation extraction method based on convolution neural network was proposed.This me-thod adds the artificial rules to the training corpus with the entity relation label,and then transforms the weak relation training corpus into the vector characteristic matrix,next inputs it into the convolution neural network for training the classification model,and finally realizes the entity relation extraction.The experimental results show that the method is more accurate and efficient than the conventional machine learning method.