SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–110 of 247 papers

TitleStatusHype
Finding a Character's Voice: Stylome Classification on Literary Characters—0
French and German Corpora for Audience-based Text Type Classification—0
From high heels to weed attics: a syntactic investigation of chick lit and literature—0
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings—0
Deep Learning of Nonnegativity-Constrained Autoencoders for Enhanced Understanding of Data—0
Automatic Classification by Topic Domain for Meta Data Generation, Web Corpus Evaluation, and Corpus Comparison—0
GPKEX: Genetically Programmed Keyphrase Extraction from Croatian Texts—0
Graph-based Semi-Supervised Learning Algorithms for NLP—0
Handling Imbalanced Dataset in Multi-label Text Categorization using Bagging and Adaptive Boosting—0
An Examination of Regret in Bullying Tweets—0
Show:102550
← PrevPage 11 of 25Next →

No leaderboard results yet.