基于词权的类指数空间密度古兰经相关意义排序

2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2016-10-01 DOI:10.1109/ICACSIS.2016.7872753

Kurniawati, A'la Syauqi

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引用次数: 3

摘要

目前，基于特定查询的信息检索已广泛应用于计算机系统中。其中一种比较流行的方法是基于TF的向量空间模型(Vector Space Model, SVM)。IDF term-weighting。本文采用TF.IDF。提出了基于ICSδF项加权的分类索引方法，并将其与分类索引法的有效性进行了比较。IDF和TF.IDF.ICF项加权。每种方法都通过《古兰经》数据集进行调查。《古兰经》由许多经文组成，每节经文都是一个单独的文档，根据用户的查询进行排名。实验结果表明，该方法可用于文档排序，准确率达93%，优于现有方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Term weighting based class indexes using space density for Al-Qur'an relevant meaning ranking

Nowadays information retrieval based on specific queries is already used in computer system. One of the popular methods is document ranking using Vector Space Model (SVM) based on TF.IDF term-weighting. In this paper TF.IDF.ICSδF term-weighting based class-indexing is proposed, afterward comparing its effectiveness to TF.IDF and TF.IDF.ICF term weighting. Each method is investigated through Al-Qur'an dataset. Al-Qur'an consist many verses, each verse of the Al-Qur'an is a single document which is ranked based on user query. The experimental show that the proposed method can be implemented on document ranking and the performance is better than previous methods with accurate value 93%.

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2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS)

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