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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
Sequential Learning of Convolutional Features for Effective Text Classification—0
Multi-Granular Text Encoding for Self-Explaining Categorization—0
Text Categorization by Learning Predominant Sense of Words as Auxiliary TaskCode0
Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings—0
Leap-LSTM: Enhancing Long Short-Term Memory for Text CategorizationCode0
Heterogeneous Multi-task Metric Learning across Multiple Domains—0
Rep the Set: Neural Networks for Learning Set RepresentationsCode0
Learning with Inadequate and Incorrect Supervision—0
Learning Graph Pooling and Hybrid Convolutional Operations for Text RepresentationsCode0
Trigonometric comparison measure: A feature selection method for text categorization—0
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