| 摘要: |
| 为了提高互联网上新闻事件在线检测的效率,利用加窗策略、命名实体识别及后缀树聚类等技术提出了一种新的检测算法.该算法基于实体识别技术解析出新闻数据特有的信息元素(例如日期、地点、人物等),并在限定的时间窗口内,通过新闻特征的语义匹配实现了新事件的快速识别,从而大幅降低了基于文本相似度计算的检测算法带来的巨大时间消耗.实验结果证明,该算法能够实现在保障检测准确率的同时显著提高检测的效率. |
| 关键词: 在线事件检测 加窗策略 命名实体识别 新闻特征 后缀树聚类 |
| DOI： |
| 分类号: |
| 基金项目:Supported by the National High-Tech Research and Development Plan of China under Grant No.2008AA01Z301 (国家高技术研究发展计划(863)) |
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| On-Line Event Detection from Web News Stream |
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FU Yan, ZHOU Ming-Quan, WANG Xue-Song, LUAN Hua
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College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
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| Abstract: |
| In order to improve the efficiency of event detection from on-line news stream, we propose a new method to accomplish detection task with window-adding, named entity recognition and suffix tree clustering. In our method, we make full use of informative elements extracted from news (such as date, place, person and so on) to help detection task, and accomplish the detection efficiently with news characteristics matching, which decreases text similarity computation greatly. Experimental results show that our method improves on-line event detection performance, without sacrificing detection precision. |
| Key words: on-line event detection window-adding strategy named entity recognition news characteristic suffix-tree clustering |
