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引用次数: 0
摘要
伪相关性反馈表明,顶部文档中的频繁词与初始查询相关。然而,与术语频率方法相关的主要缺点是它依赖于特征独立性,而忽略了文本中单词之间可能存在的任何依赖性。在本文中,我们提出了一种基于词图的查询扩展方法。它补充了项频法。在TREC WT10g测试集上,MAP(Mean Average Precision)的实验结果表明,该方法比语言模型提高了6.4%。
Query Expansion based on Word Graph using Term Proximity
The pseudo relevance feedback suggests that frequent words at the top documents are related to initial query. However, the main drawback associated with the term frequency method is the fact that it relies on feature independence, and disregards any dependencies that may exist between words in the text. In this paper, we propose query expansion based on word graph using term proximity. It supplements term frequency method. On TREC WT10g test collection, experimental results in MAP(Mean Average Precision) show that the proposed method achieved 6.4% improvement over language model.