减轻文档平滑的负担,实现更好的语言建模检索

Mark D. Smucker, James Allan
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引用次数: 9

摘要

我们假设,如果我们减少对文档平滑的需求,以提供类似于逆文档频率(IDF)的效果,那么语言建模检索将得到改善。我们创建了逆收集频率(ICF)加权查询模型,作为将类似idf的角色与文档平滑部分分离的工具。与最大似然估计(MLE)查询相比,ICF加权查询在描述查询的平均精度方面提高了6.4%。与MLE查询相比,ICF加权查询在较少文档平滑的情况下执行得更好。语言建模检索可能受益于在文档平滑之外单独合并类似idf的行为的方法。
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Lightening the load of document smoothing for better language modeling retrieval
We hypothesized that language modeling retrieval would improve if we reduced the need for document smoothing to provide an inverse document frequency (IDF) like effect. We created inverse collection frequency (ICF) weighted query models as a tool to partially separate the IDF-like role from document smoothing. Compared to maximum likelihood estimated (MLE) queries, the ICF weighted queries achieved a 6.4\% improvement in mean average precision on description queries. The ICF weighted queries performed better with less document smoothing than that required by MLE queries. Language modeling retrieval may benefit from a means to separately incorporate an IDF-like behavior outside of document smoothing.
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