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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 41–50 of 247 papers

TitleStatusHype
Leap-LSTM: Enhancing Long Short-Term Memory for Text CategorizationCode0
Learning Graph Pooling and Hybrid Convolutional Operations for Text RepresentationsCode0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
Revisiting neural relation classification in clinical notes with external informationCode0
Aspect and Sentiment Aware Abstractive Review Summarization—0
A Semi-supervised Approach for Natural Language Call Routing—0
Arabic Text Categorization Algorithm using Vector Evaluation Method—0
A Method of Accounting Bigrams in Topic Models—0
A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing—0
A Proposal of Automatic Error Correction in Text—0
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