改进的k近邻分类在主题跟踪中的应用

HongXiang Diao, Zhansheng Bai, Xilin Yu
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引用次数: 8

摘要

新闻话题跟踪是TDT的主要任务之一,其目的是监控新闻故事流,并提前识别和给出与多个新闻故事所描述的主题相关的后续故事。本文详细阐述了主题跟踪的概念和目前常用的跟踪方法;针对训练实例的稀缺性,本文对传统KNN分类法进行了有效改进,并将其应用于主题跟踪;此外,在主题跟踪过程中加入了时间窗策略,有效降低了计算复杂度。最后的实验结果也证明了该方法优于传统的KNN主题跟踪方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Notice of RetractionThe application of improved K-Nearest Neighbor classification in topic tracking
News topic tracking is one of the major tasks of TDT, the aim of which is monitoring news story flow as well as recognizing and giving subsequent stories in advance related to topics described by several news stories. This paper explains in details the concept of topic tracking and the common methods at present; aiming at the scarcity of positive example of training, this paper makes effective improvement on traditional KNN taxonomy and applies it to topic tracking; besides, it adds time window strategy to the process of topic tracking, which effectively reduces calculation complexity. The final experimental result also proves that this method is superior to traditional KNN topic tracking method.
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