Spelling correction is considered a challenging task for resource-scarce languages. The Arabic language is one of these resource-scarce languages, which suffers from the absence of a large spelling correction dataset, thus datasets injected with artificial errors are used to overcome this problem. In this paper, we trained the Text-to-Text Transfer Transformer (T5) model using artificial errors to correct Arabic soft spelling mistakes. Our T5 model can correct 97.8% of the artificial errors that were injected into the test set. Additionally, our T5 model achieves a character error rate (CER) of 0.77% on a set that contains real soft spelling mistakes. We achieved these results using a 4-layer T5 model trained with a 90% error injection rate, with a maximum sequence length of 300 characters.
The purpose of this paper to discriminate between the poetic poems of each poet depending on the characteristics and attribute of the Arabic letters. Four categories used for the Arabic letters, letters frequency have been included in a multidimensional contingency table and each dimension has two or more levels, then contingency coefficient calculated.
The paper sample consists of six poets from different historical ages, and each poet has five poems. The method was programmed using the MATLAB program, the efficiency of the proposed method is 53% for the whole sample, and between 90% and 95% for each poet's poems.
This research is intended to high light the uses of political content in foreign Arabic / speaking websites, such as “ CNN “ and” Euro News“, The research problem stems from the main question: What is the nature of the use of the websites in the political content provided through them? A set of sub-questions that give the research aspects and aims to achieve a set of objectives , including the identification of topics that included , the political content provided through , the sample sites during the time period for analysis and determine that the study uses descriptive research based on the discovery of the researcher, describing it accurately and defining the relations between the components.
The research conducted the des
Objective(s): The aim of the study was to identify the prevalence of overweight and obesity in adolescence and
to estimate the effect of socio- demographic and health behaviors that predicting obesity in adolescents.
Methodology: A cross-sectional descriptive study was being carried out at three public Arabic secondary
schools in Erbil city from October 1
st 2010 to January 30th 2011. A systematic randomly sample size of 461 students
was selected.
Results: In this study, the age of (46.2%, 122) of males students were ranged between (17- 18.9) years old compared
to females students (74.1%, 146) their age ranged between (15 -16.9) years old. About (3.4%, 9) of males
adolescents having overweight while all female ado
Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to
... Show MoreSentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniques. Such issues are related to employing robust features that have the ability to discrimina
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