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Arabic Web Pages Clustering And Annotation Using Semantic Class Features

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Alghamdi, Hanan M. (Author)
مؤلفين آخرين: Abdul Karim, Nor Shahriza (Co-Author) , Selamat, Ali (Co-Author)
المجلد/العدد: مج26, ع4
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2014
الصفحات: 388 - 397
DOI: 10.33948/0584-026-004-005
ISSN: 1319-1578
رقم MD: 973353
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
كلمات المؤلف المفتاحية:
k-Means | Semantic Similarity | Text Clustering | Arabic Webpage
رابط المحتوى:
صورة الغلاف QR قانون
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LEADER 02498nam a22002537a 4500
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024 |3 10.33948/0584-026-004-005 
041 |a eng 
044 |b السعودية 
100 |9 524792  |a Alghamdi, Hanan M.  |e Author 
245 |a Arabic Web Pages Clustering And Annotation Using Semantic Class Features 
260 |b جامعة الملك سعود  |c 2014 
300 |a 388 - 397 
336 |a بحوث ومقالات  |b Article 
520 |b To effectively manage the great amount of data on Arabic web pages and to enable the classification of relevant information are very important research problems. Studies on sentiment text mining have been very limited in the Arabic language because they need to involve deep semantic processing. Therefore, in this paper, we aim to retrieve machine-understandable data with the help of a Web content mining technique to detect covert knowledge within these data. We propose an approach to achieve clustering with semantic similarities. This approach comprises integrating k-means document clustering with semantic feature extraction and document vectorization to group Arabic web pages according to semantic similarities and then show the semantic annotation. The document vectorization helps to transform text documents into a semantic class probability distribution or semantic class density. To reach semantic similarities, the approach extracts the semantic class features and integrates them into the similarity weighting schema. The quality of the clustering result has evaluated the use of the purity and the mean intra-cluster distance (MICD) evaluation measures. We have evaluated the proposed approach on a set of common Arabic news web pages. We have acquired favorable clustering results that are effective in minimizing the MICD, expanding the purity and lowering the runtime. 
653 |a صفحات الويب  |a اللغة العربية  |a التشابه الدلالي 
692 |b k-Means  |b Semantic Similarity  |b Text Clustering  |b Arabic Webpage 
700 |9 524795  |a Abdul Karim, Nor Shahriza  |e Co-Author 
700 |9 524794  |a Selamat, Ali  |e Co-Author 
773 |c 005  |e Journal of King Saud University (Computer and Information Sciences)  |f Maǧalaẗ ǧamʼaẗ al-malīk Saud : ùlm al-ḥasib wa al-maʼlumat  |l 004  |m مج26, ع4  |o 0584  |s مجلة جامعة الملك سعود - علوم الحاسب والمعلومات  |v 026  |x 1319-1578 
856 |u 0584-026-004-005.pdf 
930 |d y  |p y 
995 |a science 
999 |c 973353  |d 973353 

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