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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 51–60 of 247 papers

TitleStatusHype
A Proposal of Automatic Error Correction in Text—0
BERT-based Chinese Text Classification for Emergency Domain with a Novel Loss Function—0
A Practical Perspective on Latent Structured Prediction for Coreference Resolution—0
Can characters reveal your native language? A language-independent approach to native language identification—0
Categorization of Turkish News Documents with Morphological Analysis—0
Character-based recurrent neural networks for morphological relational reasoning—0
Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words—0
Cluster Based Symbolic Representation for Skewed Text Categorization—0
A Machine Learning Method to Distinguish Machine Translation from Human Translation—0
BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network—0
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