文献类型：期刊文献

中文题名：一种结合主题模型与段落向量的短文本聚类方法

英文题名：A Short Text Clustering Method Combining Topic Model and Paragraph Vector

作者：饶毓和[1];凌志浩[1]

机构：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237

年份：2020

卷号：46

期号：3

起止页码：419

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

语种：中文

中文关键词：词对主题模型;段落向量;局部离群因子;JS散度;短文本聚类

外文关键词：BTM;paragraph vector;local outlier factor;JS divergence;short text clustering

摘要：为了克服短文本的稀疏性和高维度性,同时提升文本聚类质量,提出了一种结合词对主题模型(Biterm Topic Model, BTM)与段落向量(Paragraph Vector, PV)的短文本聚类方法。该方法主要包括两个重要步骤:一是利用由词对主题模型所求出的词-文档-主题概率分布,并结合局部离群因子与JS散度对整个文本集合中的词语进行语义拆分;二是将经过词语语义拆分后的文本输入至向量化模型PV-DBOW(Distributed Bag of Words Version of Paragraph Vector)得到段落向量,并将其与对应的文档-主题概率分布拼接起来构成文本特征向量。实验结果表明,本文方法得到的特征向量对短文本具有较强的区分能力,能有效改善短文本的聚类效果,同时也能避免受到短文本的稀疏性影响。
As a hot topic in the domain of text information mining,text clustering has been widely used in information retrieval,search engines,etc.However,short text has high dimensionality and sparsity of features,which make the clustering on short text face many challenges.Although a lot of efforts have been made to overcome the sparsity and high dimensionality of short text,there still exist many problems on how to further improve the quality of short text clustering.To this end,by means of biterm topic model(BTM)and paragraph vector(PV),this poper proposes a short text clustering method from the perspective of statistics and neural network algorithm,which includes two main steps:(1)Utilize the obtained word-document-topic probability distribution via BTM,in combination with the local outlier factor and JS divergence,to achieve semantic splitting of the words in the entire set of texts;(2)Input the above texts into the vectorization model PV-DBOW(Distributed Bag of Words version of Paragraph Vector)to obtain the paragraph vectors,which will further be combined with their corresponding document-topic probability distribution to form the eigenvectors of text feature.The proposed method can enrich the information carried by text features and ensures a low feature dimension.Finally,it has been shown via the experimental results that the obtained eigenvectors have stronger discriminative ability for short texts such that the clustering effect of short texts can be effectively improved,meanwhile,the negative effects of sparseness in short texts may be avoided.