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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 191–200 of 247 papers

TitleStatusHype
TPT: An Empirical Term Selection for Arabic Text Categorization—0
Transductive Adaptation of Black Box Predictions—0
Trigonometric comparison measure: A feature selection method for text categorization—0
Tuning Traditional Language Processing Approaches for Pashto Text Classification—0
Tunisian dialect Wordnet creation and enrichment using web resources and other Wordnets—0
Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy—0
UnibucKernel: An Approach for Arabic Dialect Identification Based on Multiple String Kernels—0
Using Linear Classifiers for the Automatic Triage of Posts in the 2016 CLPsych Shared Task—0
Using Word Embeddings for Italian Crime News Categorization—0
UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination—0
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