Clustering-Based Online News Topic Detection and Tracking Through Hierarchical Bayesian Nonparametric Models

被引：7

作者：

Fan, Wentao ^{[1
]}

Guo, Zhiyan ^{[1
]}

Bouguila, Nizar ^{[2
]}

Hou, Wenjuan ^{[3
]}

机构：

[1] Huaqiao Univ, Dept Comp Sci & Technol, Xiamen, Fujian, Peoples R China

[2] Concordia Univ, CIISE, Montreal, PQ, Canada

[3] Huaqiao Univ, Instrumental Anal Ctr, Xiamen, Fujian, Peoples R China

来源：

SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL | 2021年

基金：

中国国家自然科学基金;

关键词：

Clustering; topic detection and tracking; hierarchical Bayesian model; VARIATIONAL INFERENCE; SELECTION;

D O I：

10.1145/3404835.3462982

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we propose a clustering-based online news topic detection and tracking (TDT) approach based on hierarchical Bayesian nonparametric framework that allows topics to be shared across different news stories in a corpus. Our approach is formulated using the hierarchical Pitman-Yor process mixture model with the inverted Beta-Liouville (IBL) distribution as its component density, which has shown superior performance in modeling text data than the widely used Gaussian distribution. Moreover, we theoretically develop a convergence-guaranteed online learning algorithm that can effectively learn the proposed TDT model from a stream of news stories based on varational Bayes. The merits of our TDT approach are illustrated by comparing it with other well-defined clustering-based TDT approaches on different news data sets.

引用

页码：2126 / 2130

页数：5

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