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Text Classification
Text classification involves assigning labels to text that denote a particular category, for example whether the sentiment expressed in this text is...
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An enrichment multi-layer Arabic text classification model based on siblings patterns extraction
Ontologies extraction is the cornerstone for a meaningful knowledge representation. Ontologies represent the semantic relations repository in a...
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Text Classification
This chapter describes the entire process of text classification based on supervised learning. Each and every step will be explained with the help of...
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Automated Text Psychodiagnostics and the Problem of Monitoring Social Networks
AbstractThe article presents a review of the results of the automated text psychodiagnostics obtained using the intellectual text analysis tool...
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Business text classification with imbalanced data and moderately large label spaces for digital transformation
Digital transformation refers to an organization’s use of digital technology to improve its products, services, and operations, aligning them with...
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Deep Convolutional Neural Network for Knowledge-Infused Text Classification
Deep neural networks are extensively used in text mining and Natural Language Processing is to enable computers to understand, analyze, and generate...
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Towards an Automated Classification of Software Libraries
Nowadays, the use of third-party libraries in software is common. At the same time, the number of published libraries continues to increase. An...
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A Systematic survey on automated text generation tools and techniques: application, evaluation, and challenges
Automatic text generation is the generation of natural language text by machines. Enabling machines to generate readable and coherent text is one of...
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Text classification models for personality disorders identification
This research focuses on identifying personality disorders in individuals using their social media text. We developed a unique collection of words...
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An optimal feature selection method for text classification through redundancy and synergy analysis
Feature selection is an essential step in text classification tasks to enhance model performance, reduce computational complexity, and mitigate the...
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Data augmentation and adversary attack on limit resources text classification
Data Augmentation and Adversary Attack in text are complex techniques based on the generation of new instances. This is performed by introducing some...
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Sentiment classification in Hindi text using hybrid deep learning method
Sentiment analysis (SA) gives the tool to the researcher to evaluate the sentiments of different users in multiple languages in online discourse. In...
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Label prompt for multi-label text classification
Multi-label text classification has been widely concerned by scholars due to its contribution to practical applications. One of the key challenges in...
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Automated retinal disease classification using hybrid transformer model (SViT) using optical coherence tomography images
Optical coherence tomography (OCT) is a widely used imaging technique in ophthalmology for diagnosis and treatment. Recent advances in deep neural...
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Prostate classification network (PC-Net) for automated classification of Prostate cancer in Magnetic resonance imaging
Prostate cancer (PCa) is found to be the second most common cause of death in men after lung cancer, making it necessary to diagnose as early as...
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Uncertainty Quantification for Text Classification
This half-day tutorial introduces modern techniques for practical uncertainty quantification specifically in the context of multi-class and...
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Text classification based on optimization feature selection methods: a review and future directions
A substantial portion of today’s multimedia data exists in the form of unstructured text. However, the unstructured nature of text poses a...
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A review of semi-supervised learning for text classification
A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To...
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Simple Framework for Interpretable Fine-Grained Text Classification
Fine-grained text classification with similar and many labels is a challenge in practical applications. Interpreting predictions in this context is...
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Automated generation of text handles from scanned images of scholarly articles for indexing in digital archive
There have been extensive studies and rapid improvements in automated document categorization, document retrieval, document recommendations, etc....
