کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
483895 702868 2014 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Arabic web pages clustering and annotation using semantic class features
ترجمه فارسی عنوان
خوشه بندی صفحات وب عربی و حاشیه نویسی با استفاده از ویژگی های کلاس معنایی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of King Saud University - Computer and Information Sciences - Volume 26, Issue 4, December 2014, Pages 388–397
نویسندگان
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