Exploring term proximity statistic for Arabic information retrieval

Abdelkader El Mahdaouy, Éric Gaussier, Said Ouatik El Alaoui
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引用次数: 12

Abstract

Term proximity statistic, which consists of rewarding documents where the matched query terms occur in close proximity, has proved its effectiveness in document retrieval performance. However, this field of research remains unexplored for Arabic information retrieval (IR) despite of the non diacritical text and the rich morphology of Arabic language which complicate the retrieval process. In this paper, we propose to boost the Arabic information retrieval performance by using proximity information. Our aim is to evaluate proximity features for Arabic language in order to go beyond the bag-of-words, and to overcome the problems related to text preprocessing. We investigate several state-of-the-art proximity models, including the Cross-Term model (CRTER), the Markov Random Field model (MRF), the divergence from randomness (DFR) multinomial model, and the Positional Language Model (PLM). For preprocessing purposes, Khoja and light stemming algorithms have been used. Experiments are performed on the Arabic TREC-2001/2002 collection using Terrier IR platform. The obtained results show significant improvements by using proximity based-models for Arabic IR.
探索阿拉伯语信息检索中的术语接近统计
术语接近统计由匹配的查询术语出现在接近位置的文档奖励组成,已经证明了其在文档检索性能方面的有效性。然而,尽管阿拉伯语的非变音文本和丰富的阿拉伯文词法使检索过程复杂化,但这一领域的研究仍未得到探索。本文提出利用邻近信息来提高阿拉伯文信息检索的性能。我们的目标是评估阿拉伯语的接近特征,以超越词袋,并克服与文本预处理相关的问题。我们研究了几种最先进的接近模型,包括交叉项模型(CRTER)、马尔可夫随机场模型(MRF)、随机性散度(DFR)多项模型和位置语言模型(PLM)。为了进行预处理,使用了Khoja和light词干提取算法。实验采用Terrier红外平台,在阿拉伯语TREC-2001/2002样品上进行。结果表明,基于接近度的阿拉伯语红外模型具有显著的改进效果。
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