Axiomatic Analysis of Smoothing Methods in Language Models for Pseudo-Relevance Feedback
Axiomatic Analysis of Smoothing Methods in Language Models for Pseudo-Relevance Feedback
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DOI：
10.1145/2808194.2809471
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发表时间：
2015-09
期刊：
影响因子：
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通讯作者：
Hussein Hazimeh;ChengXiang Zhai
中科院分区：
文献类型：
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作者：
Hussein Hazimeh;ChengXiang Zhai
伪相关反馈（PRF）是一种重要的通用技术，可以在不需要用户任何努力的情况下提高检索效率。几个最先进的PRF模型基于语言建模方法，其中查询语言模型是基于反馈文档学习的。在所有这些模型中，反馈文档都是用集合语言模型平滑的一元语言模型表示的。虽然基于集合语言模型的平滑已被证明在使用语言模型进行检索时是有效和必要的，但我们使用公理分析表明，这种平滑方案固有地导致反馈模型倾向于频繁项，从而违反了确保选择判别反馈项所需的IDF约束。为了解决这个问题，我们提出在反馈阶段用加性平滑取代基于集合语言模型的平滑，分析表明，加性平滑可以选择更多的判别项。实证评估进一步证实，在基于多语言模型的PRF模型中，加性平滑确实显著优于基于集合的平滑方法。
Pseudo-Relevance Feedback (PRF) is an important general technique for improving retrieval effectiveness without requiring any user effort. Several state-of-the-art PRF models are based on the language modeling approach where a query language model is learned based on feedback documents. In all these models, feedback documents are represented with unigram language models smoothed with a collection language model. While collection language model-based smoothing has proven both effective and necessary in using language models for retrieval, we use axiomatic analysis to show that this smoothing scheme inherently causes the feedback model to favor frequent terms and thus violates the IDF constraint needed to ensure selection of discriminative feedback terms. To address this problem, we propose replacing collection language model-based smoothing in the feedback stage with additive smoothing, which is analytically shown to select more discriminative terms. Empirical evaluation further confirms that additive smoothing indeed significantly outperforms collection-based smoothing methods in multiple language model-based PRF models.