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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 241247 of 247 papers

TitleStatusHype
SemSim: Resources for Normalized Semantic Similarity Computation Using Lexical Networks0
Inverse-Category-Frequency based supervised term weighting scheme for text categorizationCode0
Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization0
Semi-supervised Learning with Weakly-Related Unlabeled Data : Towards Better Text Categorization0
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scalesCode0
Latent Dirichlet AllocationCode1
A Sequential Algorithm for Training Text ClassifiersCode0
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