基于狄利克雷多项分配模型的多源文本主题挖掘模型  

Multi-source text topic mining model based on Dirichlet multinomial allocation model

作　　者：徐立洋 黄瑞章[1,2,3] 陈艳平 钱志森 黎万英 XU Liyang;HUANG Ruizhang;CHEN Yanping;QIAN Zhisen;LI Wanying(College of Computer Science and Technology,Guizhou University,Guiyang Guizhou 550025,China;Guizhou Provincial Key Laboratory of Public Big Data(Guizhou University),Guiyang Guizhou 550025,China;State Key Laboratory for Novel Software Technology(Nanjing University),Nanjing Jiangsu 210093,China)

机构地区：[1]贵州大学计算机科学与技术学院,贵阳550025 [2]贵州省公共大数据重点实验室(贵州大学),贵阳550025 [3]计算机软件新技术国家重点实验室(南京大学),南京210093

出　　处：《计算机应用》2018年第11期3094-3099,3104,共7页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61462011);国家自然科学基金重大研究计划项目(91746116);贵州省重大应用基础研究项目(黔科合JZ字[2014]2001);贵州省科技重大专项计划项目(黔科合重大专项字[2017]3002);贵州省自然科学基金资助项目(黔科合基础[2018]1035)~~

摘　　要：随着文本数据来源渠道越来越丰富,面向多源文本数据进行主题挖掘已成为文本挖掘领域的研究重点。由于传统主题模型主要面向单源文本数据建模,直接应用于多源文本数据有较多的限制。针对该问题提出了基于狄利克雷多项分配(DMA)模型的多源文本主题挖掘模型——多源狄利克雷多项分配模型(MSDMA)。通过考虑主题在不同数据源的词分布的差异性,结合DMA模型的非参聚类性质,模型主要解决了如下三个问题:1)能够学习出同一个主题在不同数据源中特有的词分布形式;2)通过数据源之间共享主题空间和词项空间,使得数据源间可进行主题知识互补,提升对高噪声、低信息量的数据源的主题发现效果;3)能自主学习出每个数据源内的主题数量,不需要事先给定主题个数。最后通过在模拟数据集和真实数据集的实验结果表明,所提模型比传统主题模型能更有效地对多源数据进行主题信息挖掘。With the rapid increase of text data sources,topic mining for multi-source text data becomes the research focus of text mining.Since the traditional topic model is mainly oriented to single-source,there are many limitations to directly apply to multi-source.Therefore,a topic model for multi-source based on Dirichlet Multinomial Allocation model(DMA)was proposed considering the difference between sources of topic word-distribution and the nonparametric clustering quality of DMA,namely MSDMA(Multi-Source Dirichlet Multinomial Allocation).The main contributions of the proposed model are as follows:1)it takes into account the characteristics of each source itself when modeling the topic,and can learn the source-specific word distributions of topic k;2)it can improve the topic discovery performance of high noise and low information through knowledge sharing;3)it can automatically learn the number of topics within each source without the need for human pre-given.The experimental results in the simulated data set and two real datasets indicate that the proposed model can extract topic information more effectively and efficiently than the state-of-the-art topic models.

关 键 词：多源文本数据 主题模型 吉布斯采样 狄利克雷多项分配模型 文本挖掘 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]