Joint extraction of entity and relation represents a critical component in naturallanguageprocessing, exerting a direct influence on the quality of ensuing knowledge graph construction. Although existing methodologi...
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Sentiment analysis, the process of gauging user attitudes and emotions through their textual data, including social media posts and other forms of communication, is a valuable tool for informed decision-making. In oth...
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Ontology alignment, a critical process in the Semantic Web for detecting relationships between different ontologies, has traditionally focused on identifying so-called "simple" 1-to-1 relationships through c...
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In practical scenarios of radar interference recognition, it is challenging to acquire sufficient instances of certain interference patterns, yielding a limited dataset for training recognition models. This scarcity o...
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This paper focuses on the identification and classification of Arabic Verbal Multi-Word Expressions (VMWE), which are combinations of words with a unitary meaning and containing at least one verb. We describe our cont...
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ISBN:
(纸本)9783031850660;9783031850677
This paper focuses on the identification and classification of Arabic Verbal Multi-Word Expressions (VMWE), which are combinations of words with a unitary meaning and containing at least one verb. We describe our contribution to annotating the Arabic-annotated-conll17 corpus with VMWEs, following the annotation guide of the PARSEME framework, and using the ChatGPT model to enrich the corpus with sentences that contain more linguistic phenomena. We propose a method for the identification and classification of Arabic VMWEs based on machine learning (Random Forest, Decision trees and Support Vector Machine) and deep learning (Conv1D+BiLSTM) models. The results show that our Random Forest model outperformed others in identification, while the Decision Tree model excelled in classification. Additionally, our deep learning model exhibited significant performance improvement after data enrichment with ChatGPT. These findings underscore the effectiveness of our approach in addressing this complex problem.
This research explores the development of an AI-driven assessment system designed to evaluate student knowledge while minimizing cheating in educational settings. The system operates in two distinct phases. In Phase 1...
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This article accentuates the opinion examination in view of the order of flipkart client surveys. Feeling Examination known as Feeling Man-made consciousness or Assessment Mining. Computerized reasoning (artificial in...
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This article accentuates the opinion examination in view of the order of flipkart client surveys. Feeling Examination known as Feeling Man-made consciousness or Assessment Mining. Computerized reasoning (artificial intelligence) alludes to the efficient recognizable proof, extraction, measurement, and investigation of emotional states and abstract information utilizing regular language handling and text examination. An application challenge in message mining and computational phonetics research is opinion examination of item audits. Here, the relationship between Flipkart item audits and the rating of the items given by the clients is to be considered. Different machine learning methods are utilized. In the first place, the surveys can be changed into vector portrayal utilizing various strategies, i.e., Sack of-words, and TF-IDF. Then, train the system by applying the Strategic Relapse, XGBoost, and Decision Trees. From that point forward, assess the models utilizing F1-Score. Thus, opinion examination is unmistakably applied to audits, review reactions, web and virtual entertainment, and medical care assets for purposes going from showcasing to client assistance to clinical medication.
With the enormous growth of social data in recent years, fake news detection has gained increasing research attention and has been widely explored in various languages. Arabic language nature imposes various challenge...
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ISBN:
(纸本)9783031850660;9783031850677
With the enormous growth of social data in recent years, fake news detection has gained increasing research attention and has been widely explored in various languages. Arabic language nature imposes various challenges, such as the complicated morphological structure and the limited resources. Therefore, the current state-of-the-art methods for fake news detection remain to be enhanced, which inspired us to explore the application of the emerging deep-learning architecture to Arabic text classification. In this paper, we present an ensemble model that enhances the attention mechanism to detect fake news in Arabic sentences. An attention mechanism unit is incorporated to highlight the critical information from the contextual feature vectors. The context-related vectors generated by the attention mechanism layers are then concatenated and passed into a classifier to predict the final label. The experimental results show that the attention mechanism improves the model's performance while yielding 99.8% in accuracy.
Machine translation is a hard problem in naturallanguageprocessing (NLP). Today, based on deep learning, the quality of machine translation systems is increasing strongly in rich-resource language pairs, but it is l...
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Pre-trained language models have made significant strides in naturallanguageprocessing tasks, enabling flexible fine-tuning for downstream applications. However, in few-shot learning scenarios, pre-trained models fa...
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