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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 61–70 of 247 papers

TitleStatusHype
A Machine Learning Method to Distinguish Machine Translation from Human Translation—0
Complex Decomposition of the Negative Distance kernel—0
BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network—0
Bayesian Kernel Methods for Natural Language Processing—0
A Personalized Markov Clustering and Deep Learning Approach for Arabic Text Categorization—0
Baselines and Bigrams: Simple, Good Sentiment and Topic Classification—0
Balancing Between Over-Weighting and Under-Weighting in Supervised Term Weighting—0
An Unsupervised Morphological Criterion for Discriminating Similar Languages—0
A Machine Learning Framework for Authorship Identification From Texts—0
A comparison of latent semantic analysis and correspondence analysis of document-term matrices—0
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