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High performance query expansion using adaptive co-training

机译：使用自适应协同训练的高性能查询扩展

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摘要

The quality of feedback documents is crucial to the effectiveness of query expansion (QE) in ad hoc retrieval. Recently, machine learning methods have been adopted to tackle this issue by training classifiers from feedback documents. However, the lack of proper training data has prevented these methods from selecting good feedback documents. In this paper, we propose a new method, called AdapCOT, which applies co-training in an adaptive manner to select feedback documents for boosting QE's effectiveness. Co-training is an effective technique for classification over limited training data, which is particularly suitable for selecting feedback documents. The proposed AdapCOT method makes use of a small set of training documents, and labels the feedback documents according to their quality through an iterative process. Two exclusive sets of term-based features are selected to train the classifiers. Finally, QE is performed on the labeled positive documents. Our extensive experiments show that the proposed method improves QE's effectiveness, and outperforms strong baselines on various standard TREC collections.
机译：反馈文档的质量对于临时检索中查询扩展（QE）的有效性至关重要。近来，已经通过训练来自反馈文档的分类器来采用机器学习方法来解决该问题。但是，缺乏适当的培训数据使这些方法无法选择好的反馈文档。在本文中，我们提出了一种称为AdapCOT的新方法，该方法以自适应方式应用协同训练来选择反馈文档以提高QE的有效性。协同训练是一种有效的技术，可用于对有限的训练数据进行分类，特别适用于选择反馈文档。所提出的AdapCOT方法利用了一小套培训文档，并通过迭代过程根据反馈文档的质量对反馈文档进行标记。选择两个基于术语的排他性功能集来训练分类器。最后，对标记的肯定文件进行质量检查。我们广泛的实验表明，所提出的方法提高了量化宽松的有效性，并且在各种标准TREC集合中均优于强基准。

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  • 来源
    《Information Processing & Management》 |2013年第2期|441-453|共13页
  • 作者

    Jimmy Xiangji Huang; Jun Miao; Ben He;

  • 作者单位

    Information Retrieval and Knowledge Management Research Lab, School of Information Technology, York University, Toronto, Canada;

    Information Retrieval and Knowledge Management Research Lab, School of Information Technology, York University, Toronto, Canada;

    Information Retrieval and Knowledge Management Research Lab, School of Information Technology, York University, Toronto, Canada;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    co-training; query expansion; relevance feedback;

    机译：共同训练查询扩展;相关性反馈;

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