News Topic Tracking and Re-ranking with Query Expansion Based on Near-Duplicate Detection

被引：0

作者：

Wu, Xiaomeng ^{[1
]}

Ide, Ichiro ^{[2
]}

Satoh, Shin'ichi ^{[1
]}

机构：

[1] Res Org Informat & Syst, Natl Inst Informat, Chiyoda Ku, 2-1-2 Hitotsubashi, Tokyo 1018430, Japan

[2] Nagoya Univ, Grad Sch, Nagoya, Aichi 4648630, Japan

来源：

ADVANCES IN MULTIMEDIA INFORMATION PROCESSING - PCM 2009 | 2009年 / 5879卷

关键词：

News Topic Tracking; News Topic Re-ranking; Near-Duplicate Detection; Video Data Mining;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Increase of digital storage capacity enabled the creation of huge-scale news video archives To make hill use of the archive, it, is necessary to grasp the development. and dependencies of news stories Considering this problem we investigate tracking and re-ranking methodologies of news stories The archive used as a test-bed consists of more than 30,000 news stories This paper proposes a novel scheme of mining topic-related stories through a query-expansion algorithm on the basis of neat duplicates built on top of text. Experiments showed that the query-expansion algorithm based on near-duplicate constraints outperformed traditional methods that only use textual features

引用

页码：755 / +

页数：2

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