Graph-Based Term Weighting for Document Ranking
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    Abstract:

    The core work of information retrieval including document classification and ranking operations, how to effectively compute the term weight of every document is one of a key technology. Use of the word co-occurrence relationship to create a text graph for each document, based on the idea of the importance of interaction between adjacent words, combining the characteristics of the word document word frequency characteristics, we iteratively compute weighting of each word. Further combining the global properties of text graph, such as density, we could rank the results of information retrieval. Experiments confirmed that the algorithm in standard data sets with good results.

    Reference
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    3 张瑜,张德贤.一种改进的特征权重算法.计算机工程,2011,37(5):210-212.
    4 陈翀,彭波,闫宏飞.一种词汇共现算法及共现词对检索系统 排序的影响. 清华大学学报(自然科学版),2005,45(S1):1857-1860.
    5 周进华,刘贵全.基于衰减词共现图的多文档摘要研究.小型 微型计算机系统, 2009,30(1):173-177.
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黄云,洪佳明,颜一鸣.基于图的特征词权重算法及其在文档排序中的应用.计算机系统应用,2012,21(6):216-219,194

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History
  • Received:September 23,2011
  • Revised:November 14,2011
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