在传统的检索模型中,文档与查询的匹配计算主要考虑词项的统计特征,如词频、逆文档频率和文档长度,近年来的研究表明应用查询词项匹配在文档中的位置信息可以提高查询结果的准确性。如何更好地刻画查询词在文档中的位置信息并建模,是研究提高检索效果的问题之一。该文在结合语义的位置语言模型(SPLM)的基础上进一步考虑了词的邻近信息,并给出了用狄利克雷先验分布来计算邻近度的平滑策略,提出了结合邻近度的位置语言检索模型。在标准数据上的实验结果表明,提出的检索模型在性能上要优于结合语义的位置语言模型。
Abstract
In most existing retrieval models, the calculations on the relevance between the document and the query are based on the statistical features, such as within-document frequencies, inverse document frequencies, document lengths and so on. Recent studies show that the term position information can promote the precision of the query results, but how to best employ this information remains an open issue. This paper proposes to integrate the terms proximity information into the semantic positional language model(SPLM), with a Dirichlet prior distribution as smoothing measure to compute proximity. The proposed semantic positional language retrieval models with a proximity information performs better than classical semantic positional language model in the experiments.
关键词
语义位置语言模型 /
Dirichlet平滑 /
邻近度信息 /
检索模型
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Key words
semantic positional language models /
Dirichlet smooth /
proximity information /
retrieval model
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参考文献
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脚注
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基金
国家自然科学基金(60963014,61163006,61203313);江西省科技厅自然科学基金(20132BAB201038)
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