中文摘要：

新事件检测（new event detection，简称NED）的目标是从一个或多个新闻源中检测出报道一个新闻话题的第一个新闻．初步实验发现，在对不同类别的新闻报道进行新事件检测时，其不同类型的词元往往具有不同的敏感程度．而传统方法往往将所有的词元等同看待．重点研究在新事件检测模型中，对于不同词元的权重设定问题．提出利用统计方法优化不同类别新闻对于不同词性词元的权重参数；提出利用已有新闻簇信息动态更新词元权重的方法，采用在新闻之间（而非新闻与新闻簇之间）计算相似度的形式，发挥两种比较形式的优点．在Linguistic Data Consortium（LDC）公共数据集TDT2与TDT3上进行实验，实验结果表明，这两种改进方法的效果明显，性能与同类系统相比有显著提升．

英文摘要：

New event detection （NED） is aimed at detecting from one or multiple streams of news stories the one being reported on a new event （i.e. not reported previously）. Preliminary experiments show that terms of different types （e.g. Noun and Verb） have different effects for different classes of stories in determining whether or not two stories are on the same topic, Unfortunately, conventional approaches usually ignore the fact. This paper proposes a NED model utilizing two approaches to addressing the problem based on term reweighting. In the first approach, the paper proposes to employ statistics on training data to learn the model for each class of stories, and in the second, the paper proposes to adjust term weights dynamically based on previous story clusters, Experimental results on two linguistic data consortium （LDC） data sets： TDT2 and TDT3 show that both the proposed approaches can effectively improve the performance of NED task, compared to the baseline method and existing methods.