Retrieval Model Based on Word Translation Probabilities and the Degree of Association of Query Concept

Jun-Gil Kim, Kyung-Soon Lee
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Abstract

One of the major challenge for retrieval performance is the word mismatch between user`s queries and documents in information retrieval. To solve the word mismatch problem, we propose a retrieval model based on the degree of association of query concept and word translation probabilities in translation-based model. The word translation probabilities are calculated based on the set of a sentence and its succeeding sentence pair. To validate the proposed method, we experimented on TREC AP test collection. The experimental results show that the proposed model achieved significant improvement over the language model and outperformed translation-based language model.
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基于词翻译概率和查询概念关联度的检索模型
在信息检索中,用户查询和文档之间的词不匹配是影响检索性能的主要问题之一。为了解决词错配问题,在基于翻译的模型中,我们提出了一种基于查询概念关联度和词翻译概率的检索模型。单词翻译概率是基于一个句子及其后续句子对的集合来计算的。为了验证所提出的方法,我们在TREC AP测试集上进行了实验。实验结果表明,该模型在语言模型的基础上取得了显著的进步,优于基于翻译的语言模型。
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