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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

Showing 221230 of 247 papers

TitleStatusHype
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scalesCode0
Discriminating between Similar Languages using Weighted Subword FeaturesCode0
A Sequential Algorithm for Training Text ClassifiersCode0
On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment AnalysisCode0
Document Informed Neural Autoregressive Topic ModelsCode0
Beyond original Research Articles Categorization via NLPCode0
Structure-Aware Convolutional Neural NetworksCode0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
Massively Multilingual Word EmbeddingsCode0
pke: an open source python-based keyphrase extraction toolkitCode0
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