一种基于领域语义相关性挖掘的迁移学习方法

On Transferring Learning Method Based on DomainSemantic Correlation Mining

针对基于特征的迁移学习方法 TPLSA只考虑领域共享主题而忽略领域独有主题的不足,提出一种基于领域语义相关性挖掘的迁移学习方法.首先,挖掘领域共享主题与独有主题;然后,构造新特征空间,将源领域、目标领域文本在新特征空间中进行表示;最后,在新特征空间中对目标领域的文本进行分类.实验结果表明该方法具有优越性.

In order to overcome the shortcoming of feature-based transfer learning method TPLSA,which only considers the case that two domains share common topics and ignores the source specific topics,a transferring learning method based on domain semantic correlation mining,has beenproposed.Firstly,mine domain common topics and specific topics.Then construct a new feature space,express two domain texts in this new space.Finally,train classifier on source domain data and classify target domain text in new feature space.Experimental results show the superiority of proposed method in this paper.