基于DMA与特征划分的多源文本主题模型

针对传统主题模型在挖掘多源文本数据集信息时存在主题发现效果不佳的问题,设计一种基于狄利克雷多项式分配(DMA)与特征划分的多源文本主题模型.以DMA模型为基础,放宽对预先输入的主题数量的限制,为每个数据源分配专有的主题分布参数,使用Gibbs采样算法估计每个数据源的主题数量.同时,对每个数据源分配专有的噪音词分布参数以...

Multi-Source Text Topic Model Based on DMA and Feature Division

Given the poor performance exhibited by the existing topic models for mining information on multi-source text data sets,a multi-source text topic model based on Dirichlet Multinomial Allocation(DMA) and feature division is designed.This model relaxes the restrictions on the number of pre-input topics,assigns a special topic distribution parameter for each data source,and automatically estimates the number of topics for each data source by using the Gibbs sampling algorithm.In addition,the model assigns a specific noise word distribution parameter and topic-word distribution parameter for each data source.The feature words and noise words of each data source are distinguished by using the feature categorization method,and the word features of each data source are learnt to avoid the influence of the noise word set on model clustering.Experimental results show that compared with the existing topic models,the proposed model can keep the unique word features of each data source,and has better topic discovery performance as well as improved robustness.