通过连续的大规模评测，话题识别与跟踪已成为国际上自然语言处理尤其是信息检索领域的一个研究热点。
Through a series of large-scale evaluations, TDT has become a hot problem for worldwide research in the fields of natural language processing, especially in information retrieval.
针对互联网上论坛和新闻网站发布的海量自然语言文本，该文设计一个话题识别与跟踪系统，将海量的数据分类整理并聚合形成各个话题。
Topic detection and tracking (TDT) aims to develop a series of technologies for event based information organization, and hierarchical topic detection (HTD) is a new task of it.
新事件检测是话题检测与跟踪领域的一项重要研究，其任务是实时监控新闻报道流并从中识别新话题。
New event detection is an important research in the field of topic detection and tracking, and its task is real-time monitoring the stream of news stories and identifying the new topics in it.
作为话题检测与跟踪的重要研究子课题，话题跟踪针对特定话题，识别后续信息流中的相关报道。
As an important subtask of topic detection and tracking, topic tracking identifies and collects relevant stories on certain topics from information stream.
作为话题检测与跟踪的重要研究子课题，话题跟踪针对特定话题，识别后续信息流中的相关报道。
As an important subtask of topic detection and tracking, topic tracking identifies and collects relevant stories on certain topics from information stream.
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