基于语言模型的检索方法为信息检索领域开辟了一个很有前景同时也具有相当挑战性的方向.与传统检索模型相比,语言模型不仅具有良好的理论基础,而且非常灵活,经过简单的变换很容易推演出其他经典的检索模型.此外,大量的实验结果表明,该方法的检索效果优于其他检索模型,因而一经提出便受到了广大研究人员的青睐.然而当前语言模型方法的研究主要集中在单语检索任务中,很少有研究关注语言模型方法在跨语言检索中的应用,针对这个问题,本文在系统介绍基于语言模型检索方法的基础上,将语言模型方法扩展到跨语言检索任务中,介绍了两个跨语言检索模型:统计翻译模型和跨语言相关语言模型.%Language modeling approach to information retrieval is promising and challenging because of its favorable foundations in statistical theory, which can deduce other classical retrieval models easily. However the recent researches on language modeling approach focus on the task of single language retrieval, and there are few studies examining its effectiveness on cross language retrieval. In this paper, we apply language modeling approach to the task of cross language retrieval based on introducing the retrieval approaches of language models. This paper presents two cross language retrieval model: statistical translation model and cross language relevance language model, and also analyzes their effectiveness.
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