• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.10
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• pp.2895-2919
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• 2024

A key element of many Natural Language Processing (NLP) applications is Named Entity Recognition (NER). It involves categorizing and identifying text into separate categories, such as identifying a location or an individual's name. Arabic NER (ANER) is also utilized in numerous other Arabic NLP (ANLP) tasks, such as Machine Translation (MT), Question Answering (QA), and Information Extraction (IE). ANER systems can often be classified into three major groups: rule-based, Machine Learning (ML), and hybrid. This study focuses on examining ML-based ANER developments, particularly in the context of Classical Arabic, which presents unique challenges due to its complex morphological structure and limited linguistic resources. We propose a supervised approach that integrates word-level, morphological, and knowledge-based features to improve NER performance for Classical Arabic. Our method was evaluated on the CANERCorpus, a specialized dataset containing annotated texts from Classical Arabic literature. The Naive Bayes (NB) approach achieved an F-measure of 80%, with precision and recall levels at 86% and 75%, respectively. These results indicate a significant improvement over traditional methods, particularly in dealing with the intricate structure of Classical Arabic. The study highlights the potential of ML in overcoming the challenges of ANER and provides directions for further research in this domain.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.120-128
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• 2023

In this work, we have presented ATSA, a hierarchical attention deep learning model for Arabic sentiment analysis. ATSA was proposed by addressing several challenges and limitations that arise when applying the classical models to perform opinion mining in Arabic. Arabic-specific challenges including the morphological complexity and language sparsity were addressed by modeling semantic composition at the Arabic morphological analysis after performing tokenization. ATSA proposed to perform phrase-chunks sentiment embedding to provide a broader set of features that cover syntactic, semantic, and sentiment information. We used phrase structure parser to generate syntactic parse trees that are used as a reference for ATSA. This allowed modeling semantic and sentiment composition following the natural order in which words and phrase-chunks are combined in a sentence. The proposed model was evaluated on three Arabic corpora that correspond to different genres (newswire, online comments, and tweets) and different writing styles (MSA and dialectal Arabic). Experiments showed that each of the proposed contributions in ATSA was able to achieve significant improvement. The combination of all contributions, which makes up for the complete ATSA model, was able to improve the classification accuracy by 3% and 2% on Tweets and Hotel reviews datasets, respectively, compared to the existing models.

• CELLMED
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• v.7 no.4
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• pp.19.1-19.5
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• 2017

Fasad i.e. venesection is an old classical method of treatment in the Unani system of medicine. Fasad is Arabic word which means "to open". In the process, complete evacuation which drains out blood and dominating humours mixed with blood from veins. Fasad is carried out when the kamiyat (quantity) of the blood is excess in the body and patient is either exposed to the risk of developing a disease or has actually developed one. In classical literature of Unani system of medicine, physicians wrote a lot about this procedure. The details of venesection is mentioned in the given paper on the basis of classical literature including history, indications, types, amount of blood to be venesected, time, person, procedure, complications and special focus has been made on the number of vessels to be venesected and their benefits with respected to disease and condition.

• Acta Via Serica
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• v.4 no.1
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• pp.87-109
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• 2019

In the Iranian epic $K{\bar{u}}shn{\bar{a}}ma$, there is a rather interesting story that recounts how the inhabitants of $B.s{\bar{i}}l\bar{a}$ cross-breed their domesticated horses with a magical horse living in the sea in order to obtain fine-bred ones. What is even more interesting is that similar accounts are also seen in many of other classical Perso-Arabic works and Chinese sources. The regions that such events took place in mainly spread over Central Asia and western China while in $K{\bar{u}}shn{\bar{a}}ma$, the story happens in $B.s{\bar{i}}l\bar{a}$, a legendary kingdom with its historical prototype being Silla. By sorting out certain records of how ancient people sought fine horses by cross-breeding domesticated horses with wild horses that inhabited mountains and waters within Chinese sources and classical Muslim works, and comparing these accounts with similar plot lines as depicted in $K{\bar{u}}shn{\bar{a}}ma$, this paper attempts to elucidate that the story in $K{\bar{u}}shn{\bar{a}}ma$ is a result of flourishing land and maritime exchanges between East Asia and West Asia during ancient and medieval times, rather than a purely literary fiction. It was not only influenced by the horse culture that thrived over the Eurasian Steppe, but the story is also coincidentally in accordance with the fact that the nomadic zone which lies within the central Eurasian continent extends as far as the Korean Peninsula in northeast Asia.