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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 141150 of 247 papers

TitleStatusHype
Online Multi-Label Classification: A Label Compression Method0
Opinion Mining and Topic Categorization with Novel Term Weighting0
Probabilistic Modeling of Progressive Filtering0
Radical Embedding: Delving Deeper to Chinese Radicals0
Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers0
Reducing Over-generation Errors for Automatic Keyphrase Extraction using Integer Linear Programming0
Reducing Over-Weighting in Supervised Term Weighting for Sentiment Analysis0
Regularizing Text Categorization with Clusters of Words0
RTRGO: Enhancing the GU-MLT-LT System for Sentiment Analysis of Short Messages0
SA-CNN: Application to text categorization issues using simulated annealing-based convolutional neural network optimization0
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