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8 articles for “Tokenization”
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Research ArticleOpen accessEvery word in the titleEvaluating Machine Translation for Domain Specific Low-Resource Nepali-English Language Pairs: The Impact of Tokenization on Statistical and Neural TechniquesAmit Kumar Roy, Bipul Syam PurkayasthaIn the modern era, the field of Machine Translation (MT) has seen a significant shift towards Neural Machine Translation (NMT) techniques, which have surpassed traditional Statistical Machine Translation (SMT) models in terms of the quality of translation. Despite this, the efficacy of these techniques may differ based on the language combination in consideration. While SMT is somewhat more flexible in … …Journal of Computer Science, Vol. 21, Issue 12 · 24 Jan 2026 · 1,920 accessesView PDFCite
Research ArticleOpen accessIn the abstractArabic Fake News Detection Across Generational Text Representations: From Traditional Models to Transformer-Based MethodologiesNoor M. AlkudahThe rapid proliferation of fake news on Arabic social media has amplified societal and political risks, yet research on automatic detection in Arabic remains limited due to scarce datasets, morphological complexity, and underexplored preprocessing strategies. This study presents a comprehensive benchmark for Arabic fake news detection, unifying seven Machine Learning (ML) algorithms, three Deep Learning (DL) models, and a transformer-based … …Journal of Computer Science, Vol. 22, Issue 4 · 17 Apr 2026 · 1,873 accessesView PDFCite
Research ArticleOpen accessIn the abstractFake News Detection Using Weighted Fine-Tuned BERT and Sparse Recurrent Neural NetworkAsha Kathigi, Mukta Pujar, A M S Akshatha et al.Fake news refers to misinformation or false reports shared in the form of images, articles, or videos, disguised as real news to manipulate people’s opinions. Recently, fake news and rumors have spread extensively and rapidly around the world. This has led to the production and propagation of inaccurate news articles. Therefore, it is necessary to restrict the spread of fake … …Journal of Computer Science, Vol. 21, Issue 12 · 23 Jan 2026 · 2,087 accessesView PDFCite
Research ArticleOpen accessIn the abstractTwitter Sentiment Analysis Using Machine Learning and Deep Learning TechniquesSafa Khalid Al Sarayrah, Noor Mahmoud Alkudah, Hesham Yousef Al-kharabshehThis research investigates the use of Machine Learning (ML) and Deep Learning, including BiLSTM approaches, for Sentiment Analysis (SA) of consumer reviews on social media sites. Businesses are increasingly depending on online reviews to determine customer satisfaction due to social media's explosive growth. We used three classification models, the assessment of these attitudes uses Naive Bayes (NB), Support Vector Machine … …Journal of Computer Science, Vol. 21, Issue 8 · 1 Sep 2025 · 6,813 accessesView PDFCite
Research ArticleOpen accessIn the abstractImplementation of Single Candidate Loss Optimization Algorithm for Loss Optimization of Bhojpuri-English Machine Translation ModelRituraj Dixit, Sarabjeet Singh Bedi, Ibrahim Aljubayri, Mohammad Zubair KhanMachine translation of low-resource Indian languages is necessary as most of the regions still know and speak their specific dialects and are still not comfortable understanding the English language. Indian languages are morphologically rich, due to which there are two big challenges, Ambiguity and Domain adaption, which are faced by researchers during the translation. Lack of data also increases the … …Journal of Computer Science, Vol. 21, Issue 5 · 17 Apr 2025 · 5,151 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Hybrid Method of Long Short-Term Memory and Auto-Encoder Architectures for Sarcasm DetectionMohammed M. AL-Ani, Nazlia Omar, Ahmed Adil NafeaSarcasm detection is considered one of the most challenging tasks in sentiment analysis and opinion mining applications in the social media. Sarcasm identification is therefore essential for a good public opinion decision. There are some studies on sarcasm detection that apply standard word2vec model and have shown great performance with word-level analysis. However, once a sequence of terms is being … …Journal of Computer Science, Vol. 17, Issue 11 · 1 Dec 2021 · 7,187 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdverse Drug Reaction Detection Using Latent Semantic AnalysisAhmed Adil Nafea, Nazlia Omar, Mohammed M. AL-AniDetecting Adverse Drug Reactions (ADRs) is one of the important information for determining the view of the patient on one drug. Most studies have investigated the extraction of ADRs from social networks, in which users share their opinion on a particular medication. Some studies have used trigger terms to detect ADRs. Such studies showed remarkable performance in terms of extracting … …Journal of Computer Science, Vol. 17, Issue 10 · 28 Oct 2021 · 7,808 accessesView PDFCite
Research ArticleOpen accessIn the abstractA HYBRID METHOD USING LEXICON-BASED APPROACH AND NAIVE BAYES CLASSIFIER FOR ARABIC OPINION QUESTION ANSWERINGKhalid Khalifa, Nazlia OmarOpinion Question Answering (Opinion QA) is the task of enabling users to explore others opinions toward a particular service of product in order to make decisions. Arabic Opinion QA is more challenging due to its complex morphology compared to other languages and has many varieties dialects. On the other hand, there are insignificant research efforts and resources available that focus … …Journal of Computer Science, Vol. 10, Issue 10 · 13 May 2014 · 7,873 accessesView PDFCite