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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 11–20 of 247 papers

TitleStatusHype
Beyond original Research Articles Categorization via NLPCode0
Consistent Text Categorization using Data Augmentation in e-Commerce—0
Tuning Traditional Language Processing Approaches for Pashto Text Classification—0
SA-CNN: Application to text categorization issues using simulated annealing-based convolutional neural network optimization—0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
Very Large Language Model as a Unified Methodology of Text MiningCode0
Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset—0
High-performance automatic categorization and attribution of inventory catalogs—0
Monitoring Energy Trends through Automatic Information Extraction—0
Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization—0
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