• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国政府科技报告 >Study of Term Proximity and Document Weighting Normalization in Pseudo Relevance Feedback - UIUC at TREC 2009 Million Query Track
【24h】

Study of Term Proximity and Document Weighting Normalization in Pseudo Relevance Feedback - UIUC at TREC 2009 Million Query Track

机译：伪相关反馈中的术语接近度和文档加权归一化研究 - 在TREC 2009百万查询跟踪中的UIUC

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we report our experiments in the TREC 2009 Million Query Track. Our first line of study is on proximity-based feedback, in which we propose a positional relevance model (PRM) to exploit term proximity evidence so as to assign more weights to expansion words that are closer to query words in feedback documents. The second line of study is to improve the weighting of feedback documents in the relevance model by using a regression- based method to approximate the probability of relevance (and thus the name RegRM). In the third line of study, we test a supervised approach for query classification. Besides, we also evaluate a selective pseudo feedback strategy which stops pseudo feedback for precision-oriented queries and only uses it for recall-oriented ones. The proposed PRM has shown clear improvements over the relevance model for pseudo feedback, suggesting that capturing the term proximity heuristic appropriately could lead to a better feedback model. RegRM performs as well as relevance model, but no noticeable improvement is observed. Unfortunately, the proposed query classification methods appear to not work well. The results also show that the proposed selective pseudo feedback may not work well, since precision-oriented queries can also benefit from pseudo feedback, though not as much as recall-oriented queries.

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号