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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 211–220 of 247 papers

TitleStatusHype
Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive StudyCode0
Rep the Set: Neural Networks for Learning Set RepresentationsCode0
A Model Ensemble Approach with LLM for Chinese Text ClassificationCode0
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text CategorizationCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
Revisiting neural relation classification in clinical notes with external informationCode0
Neural Discourse Structure for Text CategorizationCode0
Very Large Language Model as a Unified Methodology of Text MiningCode0
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical ApplicationsCode0
Improving Arabic Text Categorization Using Transformer Training DiversificationCode0
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