基于张量递归神经网络的英文语义关系分类方法研究

摘要

语义关系分类作为当前语义技术的一个基础领域,获得广泛的关注.提出基于张量空间的递归神经网络算法,利用张量(向量-矩阵对)表示单词,获得更准确的语义分类结果.通过无监督的结构化方式训练模型,大大简便分类过程,舍弃了人工手动标注.实验表明,该算法可以有效识别语义关系,比传统算法性能提高5%以上.%Classification of semantic relationships is a basic area of semantic technology and gains wide attention.Introduces a better approach to classify semantic relationships of words,tensor recursive neural network model which uses tensor (vector-matrix pairs)to represent a single words.The model trains the data by an unsupervised and structural way,which has no more need of hand-labeled corpus and simplify the process of classification.The experiment shows that the algorithm can classify semantic relationships effectively,and the outperform improves by 5 percent.