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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 126–150 of 247 papers

TitleStatusHype
Machine learning approach for text and document mining—0
Mapping WordNet Domains, WordNet Topics and Wikipedia Categories to Generate Multilingual Domain Specific Resources—0
MaxMin Linear Initialization for Fuzzy C-Means—0
Measuring Term Informativeness in Context—0
Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks—0
Modeling Topic Dependencies in Hierarchical Text Categorization—0
Modeling Trolling in Social Media Conversations—0
Monitoring Energy Trends through Automatic Information Extraction—0
Muli-label Text Categorization with Hidden Components—0
Multi-Granular Text Encoding for Self-Explaining Categorization—0
Multi-label Text Categorization with Joint Learning Predictions-as-Features Method—0
Multivariate Gaussian Document Representation from Word Embeddings for Text Categorization—0
Negative Deceptive Opinion Spam—0
N-Gramas de Caractere como T\'ecnica de Normaliza \~ao Morfol\'ogica para L\' Portuguesa: Um Estudo em Categoriza \~ao de Textos (Character N-grams as a Morphological Normalization Technique for Portuguese Language: A Study in Text Categorization)—0
Non-Standard Words as Features for Text Categorization—0
Online Multi-Label Classification: A Label Compression Method—0
Opinion Mining and Topic Categorization with Novel Term Weighting—0
Probabilistic Modeling of Progressive Filtering—0
Radical Embedding: Delving Deeper to Chinese Radicals—0
Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers—0
Reducing Over-generation Errors for Automatic Keyphrase Extraction using Integer Linear Programming—0
Reducing Over-Weighting in Supervised Term Weighting for Sentiment Analysis—0
Regularizing Text Categorization with Clusters of Words—0
RTRGO: Enhancing the GU-MLT-LT System for Sentiment Analysis of Short Messages—0
SA-CNN: Application to text categorization issues using simulated annealing-based convolutional neural network optimization—0
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