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Text Categorization

Text Categorization is the task of automatically assigning pre-defined categories to documents written in natural languages. Several types of Text Categorization have been studied, each of which deals with different types of documents and categories, such as topic categorization to detect discussed topics (e.g., sports, politics), spam detection, and sentiment classification to determine the sentiment typically in product or movie reviews.

Source: Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

Papers

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Showing 1–10 of 247 papers

TitleStatusHype
Quantum Recurrent Neural Networks for Sequential LearningCode1
NatCat: Weakly Supervised Text Classification with Naturally Annotated ResourcesCode1
Improving Document Classification with Multi-Sense EmbeddingsCode1
Latent Dirichlet AllocationCode1
Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy—0
A Model Ensemble Approach with LLM for Chinese Text ClassificationCode0
Effects of term weighting approach with and without stop words removing on Arabic text classification—0
Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders—0
Text Categorization Can Enhance Domain-Agnostic Stopword Extraction—0
Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive StudyCode0
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