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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 211220 of 247 papers

TitleStatusHype
GPKEX: Genetically Programmed Keyphrase Extraction from Croatian Texts0
A Semi-supervised Approach for Natural Language Call Routing0
TopicSpam: a Topic-Model based approach for spam detection0
Latent Semantic Matching: Application to Cross-language Text Categorization without Alignment Information0
Text Classification from Positive and Unlabeled Data using Misclassified Data Correction0
Categorization of Turkish News Documents with Morphological Analysis0
Bridging Languages through Etymology: The case of cross language text categorization0
Cross-lingual and generic text categorization (Apprentissage d'une classification th\'ematique g\'en\'erique et cross-langue \`a partir des cat\'egories de la Wikip\'edia) [in French]0
The Story of the Characters, the DNA and the Native Language0
From high heels to weed attics: a syntactic investigation of chick lit and literature0
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