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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

Showing 3140 of 247 papers

TitleStatusHype
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled BagsCode0
Ensemble Quantile ClassifierCode0
Discriminating between Similar Languages using Weighted Subword FeaturesCode0
Authorship Attribution Using Text DistortionCode0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
An Automated Text Categorization Framework based on Hyperparameter OptimizationCode0
Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive StudyCode0
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text CategorizationCode0
Inverse-Category-Frequency based supervised term weighting scheme for text categorizationCode0
Text Ranking and Classification using Data CompressionCode0
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