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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 7180 of 247 papers

TitleStatusHype
Topic or Style? Exploring the Most Useful Features for Authorship AttributionCode0
HeLI-based Experiments in Discriminating Between Dutch and Flemish Subtitles0
A Unified RvNN Framework for End-to-End Chinese Discourse Parsing0
MaxMin Linear Initialization for Fuzzy C-Means0
Computationally efficient discrimination between language varieties with large feature vectors and regularized classifiers0
A Comparative Study on using Principle Component Analysis with Different Text Classifiers0
A Visual Quality Index for Fuzzy C-Means0
INGEOTEC at SemEval-2018 Task 1: EvoMSA and μTC for Sentiment Analysis0
Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text ClassificationCode0
The Importance of Calibration for Estimating Proportions from Annotations0
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