Papers Text Categorization

Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy

The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion. Supervised methods are adept at text categorization, yet the dynamic nature of social media discussio…

A Model Ensemble Approach with LLM for Chinese Text Classification

Automatic medical text categorization can assist doctors in efficiently managing patient information. By categorizing textual information such as patients’ descriptions of symptoms, doctors can easily find key informatio…

Effects of term weighting approach with and without stop words removing on Arabic text classification

Classifying text is a method for categorizing documents into pre-established groups. Text documents must be prepared and represented in a way that is appropriate for the algorithms used for data mining prior to classific…

Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders

In this paper, we introduce a multi-label lazy learning approach to deal with automatic semantic indexing in large document collections in the presence of complex and structured label vocabularies with high inter-label c…

Text Categorization Can Enhance Domain-Agnostic Stopword Extraction

This paper investigates the role of text categorization in streamlining stopword extraction in natural language processing (NLP), specifically focusing on nine African languages alongside French. By leveraging the Masakh…

Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive Study

In an era where the silent struggle of underdiagnosed depression pervades globally, our research delves into the crucial link between mental health and social media. This work focuses on early detection of depression, pa…

Beyond original Research Articles Categorization via NLP

This work proposes a novel approach to text categorization -- for unknown categories -- in the context of scientific literature, using Natural Language Processing techniques. The study leverages the power of pre-trained …

Consistent Text Categorization using Data Augmentation in e-Commerce

The categorization of massive e-Commerce data is a crucial, well-studied task, which is prevalent in industrial settings. In this work, we aim to improve an existing product categorization model that is already in use by…

Tuning Traditional Language Processing Approaches for Pashto Text Classification

Today text classification becomes critical task for concerned individuals for numerous purposes. Hence, several researches have been conducted to develop automatic text classification for national and international langu…

SA-CNN: Application to text categorization issues using simulated annealing-based convolutional neural network optimization

Convolutional neural networks (CNNs) are a representative class of deep learning algorithms including convolutional computation that perform translation-invariant classification of input data based on their hierarchical …

Quantum Recurrent Neural Networks for Sequential Learning

Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources. Recurrent neural netwo…

Improving Pre-Trained Weights Through Meta-Heuristics Fine-Tuning

Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classification and reconstruction, object recogni…

Very Large Language Model as a Unified Methodology of Text Mining

Text data mining is the process of deriving essential information from language text. Typical text mining tasks include text categorization, text clustering, topic modeling, information extraction, and text summarization…

Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset

The automatic categorization of crime news is useful to create statistics on the type of crimes occurring in a certain area. This assignment can be treated as a text categorization problem. Several studies have shown tha…

High-performance automatic categorization and attribution of inventory catalogs

Techniques of machine learning for automatic text categorization are applied and adapted for the problem of inventory catalog data attribution, with different approaches explored and optimal solution addressing the trade…

Monitoring Energy Trends through Automatic Information Extraction

Energy research is of crucial public importance but the use of computer science technologies like automatic text processing and data management for the energy domain is still rare. Employing these technologies in the ene…

Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization

The finite invert Beta-Liouville mixture model (IBLMM) has recently gained some attention due to its positive data modeling capability. Under the conventional variational inference (VI) framework, the analytically tracta…

TPT: An Empirical Term Selection for Arabic Text Categorization

Using Word Embeddings for Italian Crime News Categorization

Several studies have shown that the use of embeddings improves outcomes in many Natural Language Processing (NLP) activities, including text categorization. This paper focuses on how word embeddings can be used on newspa…

A Proposal of Automatic Error Correction in Text

The great amount of information that can be stored in electronic media is growing up daily. Many of them is got mainly by typing, such as the huge of information obtained from web 2.0 sites; or scaned and processing by a…