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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 110 of 247 papers

TitleStatusHype
Latent Dirichlet AllocationCode1
Improving Document Classification with Multi-Sense EmbeddingsCode1
NatCat: Weakly Supervised Text Classification with Naturally Annotated ResourcesCode1
Quantum Recurrent Neural Networks for Sequential LearningCode1
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural NetworksCode0
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data SetCode0
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled BagsCode0
Authorship Attribution Using Text DistortionCode0
Authorship Attribution Using the Chaos Game RepresentationCode0
An Automated Text Categorization Framework based on Hyperparameter OptimizationCode0
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