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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 21–30 of 247 papers

TitleStatusHype
A Novel Feature Selection and Extraction Technique for Classification—0
A Novel Term_Class Relevance Measure for Text Categorization—0
An Unsupervised Morphological Criterion for Discriminating Similar Languages—0
A Personalized Markov Clustering and Deep Learning Approach for Arabic Text Categorization—0
A Practical Perspective on Latent Structured Prediction for Coreference Resolution—0
A Proposal of Automatic Error Correction in Text—0
Arabic Text Categorization Algorithm using Vector Evaluation Method—0
A Semi-supervised Approach for Natural Language Call Routing—0
A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing—0
Analysis of opinionated text for opinion mining—0
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