Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userClassification Of Arabic Texts Using Object Properties In Databases
تصنيف النصوص العربية باستخدام الخصائص العرضية في قواعد البيانات
2330
5 41
0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
قدمنا في هذا البحث دراسة مفصلة لطرق التنقيب في البيانات النصية و الإمكانيات المتوفرة في لغة الاستعلام الإجرائية PL/SQL التي تتعامل مع قواعد بيانات أوراكل الغرضية للقيام بذلك. و من ثم قمنا ببناء نموذج تنقيب يعمل على تصنيف وثائق النصوص العربية باستخدام خوارزمية SVM لفهرستها و من ثم تحويلها إلى جداول بيانات مدخلة في جداول الحالة لتصنيفها باستخدام خوارزمية Naïve Bayes و قدمنا الاستنتاجات و التوصيات بعد تقييم النتائج التي حصلنا عليها.
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 PL / 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.
References used
AGGARWAL, CH ,2014–Data Classification Algorithms and Applications. First Edition, Taylor & Francis Group, LLC, New York, USA,64P
ALPAYDIN, E, 2010-Introduction to Machine Learning. Second Edition, Cambridge, Massachusetts London, England, 579p
BARBER,D,2010-Bayesian Reasoning and Machine Learning. First Edition, Cambridge University Press, London, England, 610p
rate research
Read More
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.
In this study, we study language change in Chinese Biji by using a classification task: classifying Ancient Chinese texts by time periods. Specifically, we focus on a unique genre in classical Chinese literature: Biji (literally notebook'' or brush n
otes''), i.e., collections of anecdotes, quotations, etc., anything authors consider noteworthy, Biji span hundreds of years across many dynasties and conserve informal language in written form. For these reasons, they are regarded as a good resource for investigating language change in Chinese (Fang, 2010). In this paper, we create a new dataset of 108 Biji across four dynasties. Based on the dataset, we first introduce a time period classification task for Chinese. Then we investigate different feature representation methods for classification. The results show that models using contextualized embeddings perform best. An analysis of the top features chosen by the word n-gram model (after bleaching proper nouns) confirms that these features are informative and correspond to observations and assumptions made by historical linguists.
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
ompare the use of sentence embeddings of different kinds (fastText, mBERT , XLM-R and Arabic-BERT), as well as traditional language features such as POS tags, dependency trees, readability scores and frequency lists for language learners. Our best results have been achieved using fined-tuned Arabic-BERT. The accuracy of our 3-way CEFR classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification respectively and 0.71 Spearman correlation for regression. Our binary difficulty classifier reaches F-1 0.94 and F-1 0.98 for sentence-pair semantic similarity classifier.
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
We use Hypergraph Attention Networks (HyperGAT) to recognize multiple labels of Chinese humor texts. We firstly represent a joke as a hypergraph. The sequential hyperedge and semantic hyperedge structures are used to construct hyperedges. Then, atten
tion mechanisms are adopted to aggregate context information embedded in nodes and hyperedges. Finally, we use trained HyperGAT to complete the multi-label classification task. Experimental results on the Chinese humor multi-label dataset showed that HyperGAT model outperforms previous sequence-based (CNN, BiLSTM, FastText) and graph-based (Graph-CNN, TextGCN, Text Level GNN) deep learning models.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
