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Arabic Book Retrieval using Class and Book Index Based Term Weighting.

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Title: Arabic Book Retrieval using Class and Book Index Based Term Weighting.
Authors: Fauzi, M. Ali1 moch.ali.fauzi@ub.ac.id, Arifin, Agus Zainal2, Yuniarti, Anny2
Source: International Journal of Electrical & Computer Engineering (2088-8708). Dec2017, Vol. 7 Issue 6, p3705-3710. 6p.
Subjects: Information retrieval software, Vector spaces, Arabic language education, Spelling reform, Semantics
Abstract: One of the most common issue in information retrieval is documents ranking. Documents ranking system collects search terms from the user and orderly retrieves documents based on the relevance. Vector space models based on TF.IDF term weighting is the most common method for this topic. In this study, we are concerned with the study of automatic retrieval of Islamic Fiqh (Law) book collection. This collection contains many books, each of which has tens to hundreds of pages. Each page of the book is treated as a document that will be ranked based on the user query. We developed class-based indexing method called inverse class frequency (ICF) and book-based indexing method inverse book frequency (IBF) for this Arabic information retrieval. Those method then been incorporated with the previous method so that it becomes TF.IDF.ICF.IBF. The term weighting method also used for feature selection due to high dimensionality of the feature space. This novel method was tested using a dataset from 13 Arabic Fiqh e-books. The experimental results showed that the proposed method have the highest precision, recall, and F-Measure than the other three methods at variations of feature selection. The best performance of this method was obtained when using best 1000 features by precision value of 76%, recall value of 74%, and F-Measure value of 75%. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Arabic Book Retrieval using Class and Book Index Based Term Weighting.
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  Data: <searchLink fieldCode="AR" term="%22Fauzi%2C+M%2E+Ali%22">Fauzi, M. Ali</searchLink><relatesTo>1</relatesTo><i> moch.ali.fauzi@ub.ac.id</i><br /><searchLink fieldCode="AR" term="%22Arifin%2C+Agus+Zainal%22">Arifin, Agus Zainal</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Yuniarti%2C+Anny%22">Yuniarti, Anny</searchLink><relatesTo>2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Dec2017, Vol. 7 Issue 6, p3705-3710. 6p.
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  Data: <searchLink fieldCode="DE" term="%22Information+retrieval+software%22">Information retrieval software</searchLink><br /><searchLink fieldCode="DE" term="%22Vector+spaces%22">Vector spaces</searchLink><br /><searchLink fieldCode="DE" term="%22Arabic+language+education%22">Arabic language education</searchLink><br /><searchLink fieldCode="DE" term="%22Spelling+reform%22">Spelling reform</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: One of the most common issue in information retrieval is documents ranking. Documents ranking system collects search terms from the user and orderly retrieves documents based on the relevance. Vector space models based on TF.IDF term weighting is the most common method for this topic. In this study, we are concerned with the study of automatic retrieval of Islamic Fiqh (Law) book collection. This collection contains many books, each of which has tens to hundreds of pages. Each page of the book is treated as a document that will be ranked based on the user query. We developed class-based indexing method called inverse class frequency (ICF) and book-based indexing method inverse book frequency (IBF) for this Arabic information retrieval. Those method then been incorporated with the previous method so that it becomes TF.IDF.ICF.IBF. The term weighting method also used for feature selection due to high dimensionality of the feature space. This novel method was tested using a dataset from 13 Arabic Fiqh e-books. The experimental results showed that the proposed method have the highest precision, recall, and F-Measure than the other three methods at variations of feature selection. The best performance of this method was obtained when using best 1000 features by precision value of 76%, recall value of 74%, and F-Measure value of 75%. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.11591/ijece.v7i6.pp3705-3710
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Vector spaces
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      – SubjectFull: Arabic language education
        Type: general
      – SubjectFull: Spelling reform
        Type: general
      – SubjectFull: Semantics
        Type: general
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      – TitleFull: Arabic Book Retrieval using Class and Book Index Based Term Weighting.
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            – D: 01
              M: 12
              Text: Dec2017
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              Y: 2017
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