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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
Structure-Aware Convolutional Neural NetworksCode0
Handling Imbalanced Dataset in Multi-label Text Categorization using Bagging and Adaptive Boosting—0
A Text Classification Application: Poet Detection from Poetry—0
Revisiting neural relation classification in clinical notes with external informationCode0
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text CategorizationCode0
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical ApplicationsCode0
SeVeN: Augmenting Word Embeddings with Unsupervised Relation VectorsCode0
Computing Word Classes Using Spectral Clustering—0
Document Informed Neural Autoregressive Topic ModelsCode0
Aspect and Sentiment Aware Abstractive Review Summarization—0
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