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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 111–120 of 247 papers

TitleStatusHype
Multivariate Gaussian Document Representation from Word Embeddings for Text Categorization—0
A Practical Perspective on Latent Structured Prediction for Coreference Resolution—0
BabelDomains: Large-Scale Domain Labeling of Lexical Resources—0
Authorship Attribution Using Text DistortionCode0
High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data—0
Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural Networks—0
Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers—0
Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization—0
Neural Discourse Structure for Text CategorizationCode0
Theoretical Foundations of Forward Feature Selection Methods based on Mutual Information—0
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