Please wait a minute...
New Technology of Library and Information Service  2011, Vol. 27 Issue (4): 52-57    DOI: 10.11925/infotech.1003-3513.2011.04.09
Current Issue | Archive | Adv Search |
Research on the Detection of Sudden Events in News Stories of Online Information
Yao Zhanlei, Xu Xin
Depatment of Informatics, East China Normal University, Shanghai 200241, China
Download: PDF(530 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  Focusing on how to capture sudden events timely and accurately, this paper introduces an idea of the Distance between two Segmental Words(DSW), and devises a model for detecting the sudden events in Internet news. This model mainly comprises two parts, as generating the Hot Element of Terms(HET) and detecting new words. Specifically, it uses the improved TF-PDF algorithm for capturing the Element of Terms(ET),which concerns to generate the Hot Element of Terms, and seeks the status quo of distribution among these terms based on the Distance between two Segmental Words, then with the relatively stable combination among these terms to achieve event detection. Experiment shows that the model has a high sensitivity on detecting the sudden events.
Key wordsEvent detection      Hot element of terms      Distance between two segmental words     
Received: 21 March 2011      Published: 11 June 2011



Cite this article:

Yao Zhanlei, Xu Xin. Research on the Detection of Sudden Events in News Stories of Online Information. New Technology of Library and Information Service, 2011, 27(4): 52-57.

URL:     OR

[1] 洪宇,张宇,范基礼,等. 基于子话题分治匹配的新事件检测[J]. 计算机学报,2008,31(4):2887-2898.

[2] Yang Y, Carbonell J G, Brown R D, et al. Learning Approaches for Detecting and Tracking News Events[J]. IEEE Intelligent Systems, 1999, 34(4):32-43.

[3] Salton G, Yang C S. On the Specification of Term Values in Automatic Indexing[J]. Journal of Documentation,1973,29(4):351-372.

[4] Brants T, Chen F, Farahat A. A System for New Event Detection[C]. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.2003:330–337.

[5] Dai X, He Y, Sun Y. A Two-layer Text Clustering Approach for Retrospective News Event Detection[C]. In: Proceedings of Artificial Intelligence and Computational Intelligence.2010:364-368.

[6] 贾自艳,何清,张海俊,等. 一种基于动态进化模型的事件探测和追踪算法[J]. 计算机研究与发展,2004, 41(7):1273-1280.

[7] 邹纲,刘洋,刘群,等. 面向Internet 的中文新词语检测[J]. 中文信息学报,2004,18(6):1-9.

[8] Bun K K, Ishizuka M. Topic Extraction from News Archive Using TF*PDF Algorithm[C]. In: Proceedings of the 3rd International Conference on Web Information Systems Engineering.2002:73-82.

[9] 雷震,吴玲达,刘宇弛,等. 基于事件的新闻报道分析技术研究进展[J]. 计算机应用研究,2007,24(5):13-16.

[10] 张阔,李涓子,吴刚,等. 基于词元再评估的新事件检测模型[J]. 软件学报,2008,19(4):817-828.

[11] Zhou M. Some Concepts and Mathematical Consideration of Similarity System Theory[J]. Journal of System Science and System Engineering,1992,1(1):84-92.

[12] 张音,王舒怀,李鹤. “概念新闻”与党报创新[J]. 新闻战线,2010(9):29-31.
[1] Li Jinhua,An Zhongjie. Analyzing Geographical Coordinates Data for Micro-blog Trending Events[J]. 现代图书情报技术, 2016, 32(2): 90-101.
[2] Zhuo Keqiu, Yu Wei, Su Xinning. Parallel Implementing Bursty Events Detection Using MapReduce[J]. 现代图书情报技术, 2015, 31(2): 46-54.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938