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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 141–150 of 247 papers

TitleStatusHype
Handling Imbalanced Dataset in Multi-label Text Categorization using Bagging and Adaptive Boosting—0
HeLI-based Experiments in Discriminating Between Dutch and Flemish Subtitles—0
Heterogeneous Multi-task Metric Learning across Multiple Domains—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
Improving Bilingual Terminology Extraction from Comparable Corpora via Multiple Word-Space Models—0
Improving K-Nearest Neighbor Efficacy for Farsi Text Classification—0
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