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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 226–247 of 247 papers

TitleStatusHype
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
Aspect and Sentiment Aware Abstractive Review Summarization—0
A Study of Association Measures and their Combination for Arabic MWT Extraction—0
A Text Classification Application: Poet Detection from Poetry—0
A Unified RvNN Framework for End-to-End Chinese Discourse Parsing—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
Automatic Corpora Construction for Text Classification—0
Automatic Generation of Language-Independent Features for Cross-Lingual Classification—0
Automatic Keyphrase Extraction: A Survey of the State of the Art—0
Automatic Keyword Extraction on Twitter—0
A Visual Quality Index for Fuzzy C-Means—0
BabelDomains: Large-Scale Domain Labeling of Lexical Resources—0
Bag Reference Vector for Multi-instance Learning—0
Balancing Between Over-Weighting and Under-Weighting in Supervised Term Weighting—0
Baselines and Bigrams: Simple, Good Sentiment and Topic Classification—0
Bayesian Kernel Methods for Natural Language Processing—0
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