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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 71–80 of 247 papers

TitleStatusHype
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization—0
Creation of Lexical Relations for IndoWordNet—0
Cross-Language Plagiarism Detection Methods—0
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
Cross-lingual Dataless Classification for Languages with Small Wikipedia Presence—0
Cross-lingual Synonymy Overlap—0
Data Mining with Shallow vs. Linguistic Features to Study Diversification of Scientific Registers—0
Deep learning models for representing out-of-vocabulary words—0
Deep Learning of Nonnegativity-Constrained Autoencoders for Enhanced Understanding of Data—0
Analysis of opinionated text for opinion mining—0
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