Topic tracking with dynamic topic model and topic-based weighting method

被引：16

作者：

Zhang X. ^{[1
]}

Wang T. ^{[1
]}

机构：

[1] Department of Computer Science and Technology, School of Computer, National University of Defense Technology, Changsha, Hunan 410073, No.137, Yanwachi Street

来源：

Journal of Software | 2010年 / 5卷 / 05期

关键词：

Dynamic topic model; Feature weighting; Topic drift; Topic tracking;

D O I：

10.4304/jsw.5.5.482-489

中图分类号：

学科分类号：

摘要：

In topic tracking, a topic is usually described by several stories. How to represent a topic is always an issue and a difficult problem in the research on topic tracking. To emphasis the topic in stories, we provide an improved topicbased tf*idf weighting method to measure the topical importance of the features in the representation model. To overcome the topic drift problem and filter the noise existed in the tracked topic description, a dynamic topic model is proposed based on the static model. It extends the initial topic model with the information from the incoming related stories and filters the noise using the latest unrelated story. The topic tracking systems are implemented on the TDT4 Chinese corpus. The experimental results indicate that both the new weighting method and the dynamic model can improve the tracking performance. © 2010 ACADEMY PUBLISHER.

引用

页码：482 / 489

页数：7

共 16 条

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