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Finding story chains in newswire articles using random walks

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Abstract

Massive amounts of information about news events are published on the Internet every day in online newspapers, blogs, and social network messages. While search engines like Google help retrieve information using keywords, the large volumes of unstructured search results returned by search engines make it hard to track the evolution of an event. A story chain is composed of a set of news articles that reveal hidden relationships among different events. Traditional keyword-based search engines provide limited support for finding story chains. In this paper, we propose a random walk based algorithm to find story chains. When breaking news happens, many media outlets report the same event. We have two pruning mechanisms in the algorithm to automatically exclude redundant articles from the story chain and to ensure efficiency of the algorithm. We further explore how named entities and word relevance can help find relevant news articles and improve algorithm efficiency by creating a co-clustering based correlation graph. Experimental results show that our proposed algorithm can generate coherent story chains without redundancy. The efficiency of the algorithm is significantly improved on the correlation graph.

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Notes

  1. https://www.mturk.com/mturk/welcome

  2. http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC95T21

  3. http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2008T19

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Authors and Affiliations

  1. CSEE Department, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA

    Xianshu Zhu & Tim Oates

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  1. Xianshu Zhu
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  2. Tim Oates
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Correspondence to Xianshu Zhu.

Additional information

This paper is an extended version of our previous paper that was published in the 13th IEEE International Conference on Information Reuse and Integration, 2012.

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Zhu, X., Oates, T. Finding story chains in newswire articles using random walks. Inf Syst Front 16, 753–769 (2014). https://doi.org/10.1007/s10796-013-9420-2

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