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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 151–160 of 247 papers

TitleStatusHype
Massively Multilingual Word EmbeddingsCode0
Complex Decomposition of the Negative Distance kernel—0
Bag Reference Vector for Multi-instance Learning—0
N-Gramas de Caractere como T\'ecnica de Normaliza \~ao Morfol\'ogica para L\' Portuguesa: Um Estudo em Categoriza \~ao de Textos (Character N-grams as a Morphological Normalization Technique for Portuguese Language: A Study in Text Categorization)—0
A Machine Learning Method to Distinguish Machine Translation from Human Translation—0
Cross-lingual Synonymy Overlap—0
Towards Improving Dialogue Topic Tracking Performances with Wikification of Concept Mentions—0
Multi-label Text Categorization with Joint Learning Predictions-as-Features Method—0
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization—0
Learning Timeline Difference for Text Categorization—0
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