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 111–120 of 247 papers

TitleStatusHype
HeLI-based Experiments in Discriminating Between Dutch and Flemish Subtitles—0
Heterogeneous Multi-task Metric Learning across Multiple Domains—0
A Joint Probabilistic Classification Model of Relevant and Irrelevant Sentences in Mathematical Word Problems—0
High-performance automatic categorization and attribution of inventory catalogs—0
High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data—0
IFlyLegal: A Chinese Legal System for Consultation, Law Searching, and Document Analysis—0
IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification—0
Improve Document Embedding for Text Categorization Through Deep Siamese Neural Network—0
Active Learning with Rationales for Text Classification—0
Deep learning models for representing out-of-vocabulary words—0
Show:102550
← PrevPage 12 of 25Next →

No leaderboard results yet.