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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 171180 of 247 papers

TitleStatusHype
Term-Class-Max-Support (TCMS): A Simple Text Document Categorization Approach Using Term-Class Relevance Measure0
Text2voronoi: An Image-driven Approach to Differential Diagnosis0
Text Categorization as a Graph Classification Problem0
Text Categorization Can Enhance Domain-Agnostic Stopword Extraction0
Text Categorization for Conflict Event Annotation0
Text Classification from Positive and Unlabeled Data using Misclassified Data Correction0
Text Retrieval by Term Co-occurrences in a Query-based Vector Space0
Text segmentation for Language Identification in Greek Forums0
Textual Relations and Topic-Projection: Issues in Text Categorization0
The Bregman Variational Dual-Tree Framework0
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