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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 31–40 of 247 papers

TitleStatusHype
A good space: Lexical predictors in word space evaluation—0
A Text Classification Application: Poet Detection from Poetry—0
A Study of Association Measures and their Combination for Arabic MWT Extraction—0
An approach based on Combination of Features for automatic news retrieval—0
A Hidden Variables Approach to Multilabel Logistic Regression—0
Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural Networks—0
Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers—0
Automatic Categorization of Tagalog Documents Using Support Vector Machines—0
Automatic Classification by Topic Domain for Meta Data Generation, Web Corpus Evaluation, and Corpus Comparison—0
Analysis of opinionated text for opinion mining—0
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