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دراسة حول تصنيف النصوص العربية باستخدام الأنطولوجيات
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نقدم في هذا البحث خوارزمية لتجميع نصوص اللغة العربية. حيث نفذنا الخوارزمية على 5 أنطولوجيات عبر برنامج بلغة الجافا، ثم عالجنا النصوص بحيث حصلنا على 338667 مفردة مع أوزانها المقابلة لكل أنطولوجيا. و قد أثبتت الخوارزمية فعاليتها في تحسين أداء المصنفات التي تم تجربتها في هذه الدراسة و هي (NB,SVM) مقارنة مع نتائج مصنفات اللغة العربية السابقة.
In this paper, we introduce an algorithm for grouping Arabic documents for building an ontology and its words. We execute the algorithm on five ontologies using Java. We manage the documents by getting 338667 words with its weights corresponding to each ontology. The algorithm had proved its efficiency in optimizing classifiers (SVM, NB) performance, which we tested in this study, comparing with former classifiers results for Arabic language.
References used
AL-Ghuribi,S Alshomrani,S. 2014. Bi-languages mining algorithm for classifying text documents (BiLTc), International Jornal of Academic Research Part A Vol. 6 No. 5, 16-25
Gruber,T. 1993. A translation approach to providing portable ontology specifications, Knowledge Acquisition, Vol.5 No 2, 199-220
Hastie,T Tibshirani,R Friedman.J. 2013-The elements of Statistical Learning - Data Mining, Inference, and Prediction. Springer-Verlag, second Ed, Berlin,764p
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اخترنا في هذا المشروع العمل على تطوير نظام يقوم بتصنيف المستندات العربية حسب محتواها, يقوم هذه النظام بالتحليل اللفظي لكلمات المستند ثم إجراء عملية Stemming"رد الأفعال إلى أصلها" ثم تطبيق عملية إحصائية على المستند في مرحلة تدريب النظام ثم بالاعتماد
على خوارزميات في الذكاء الصنعي يتم تصنيف المستند حسب محتواه ضمن عناقيد
In our research we offer detailed study of one of the data
mining functions within the text data using the object properties in
databases. It studies the possibility of applying this function on the
Arabic texts. We use procedural query language P
L / SQL that
deals with the object of Oracle databases.
Data mining model Has been built. It works on classification
of Arabic texts documents using SVM algorithm for indexing of
texts and texts preparation, Naïve Bayes algorithm to classify data
after transformation it into nested tables. So we made an evaluation
of the obtained results and conclusions.
This research is one stage of the construction of an Arabic speech synthesis
system, which is “text-to-phonemes transliteration”.
A complete text-to-phonemes transliteration system has been built for
Arabic language.
In this system we used TOPH (
Orthographic-Phonetic Transcription)
method, used for transliterating the French language, to perform the
transliteration from text to phonemes in Arabic. We also wrote the Arabic textto-
phonemes rules in TOPH formal language.
In this paper, we present a Modern Standard Arabic (MSA) Sentence difficulty classifier, which predicts the difficulty of sentences for language learners using either the CEFR proficiency levels or the binary classification as simple or complex. We c
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This paper deals with automatic detection of plagiarism in Arabic documents. We present in this paper a new idea based on the experimentation of lexical chains. The proposed method extracts those chains from original document and uses a search engine
to verify if such chains occur in other documents. The second step in our methods uses automatic translation system to translate lexical chains and verify by using search engine if those chain occurs in document in other languages. Then we compute a correlation ratio between lexical chains and lexical chains extracted from documents provided by the search engine to detect plagiarism in the original document.
We present in the end of this paper our prototype called « Alkachef » developed to detect plagiarism in Arabic document .
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