News Topic Detection Based on Capsule Semantic Graph

作     者：Shuang Yang Yan Tang Shuang Yang;Yan Tang

作者机构：the College of Computer and Information ScienceSouthwest UniversityChongqing 400000China 

出 版 物：《Big Data Mining and Analytics》 (大数据挖掘与分析（英文）)

年 卷 期：2022年第5卷第2期

页      面：98-109页

核心收录：

学科分类：05[文学] 08[工学] 081203[工学-计算机应用技术] 0503[文学-新闻传播学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：news topic detection capsule semantic graph graph kernel 

摘      要：Most news topic detection methods use word-based methods,which easily ignore the relationship among words and have semantic sparsity,resulting in low topic detection accuracy.In addition,the current mainstream probability methods and graph analysis methods for topic detection have high time complexity.For these reasons,we present a news topic detection model on the basis of capsule semantic graph(CSG).The keywords that appear in each text at the same time are modeled as a keyword graph,which is divided into multiple subgraphs through community detection.Each subgraph contains a group of closely related keywords.The graph is used as the vertex of CSG.The semantic relationship among the vertices is obtained by calculating the similarity of the average word vector of each vertex.At the same time,the news text is clustered using the incremental clustering method,where each text uses CSG;that is,the similarity among texts is calculated by the graph kernel.The relationship between vertices and edges is also considered when calculating the similarity.Experimental results on three standard datasets show that CSG can obtain higher precision,recall,and F1 values than several latest methods.Experimental results on large-scale news datasets reveal that the time complexity of CSG is lower than that of probabilistic methods and other graph analysis methods.