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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 201–210 of 247 papers

TitleStatusHype
VarClass: An Open-source Language Identification Tool for Language Varieties—0
Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization—0
A Comparative Study of Pretrained Language Models on Thai Social Text Categorization—0
Wikipedia-based Semantic Interpretation for Natural Language Processing—0
A Comparative Study on using Principle Component Analysis with Different Text Classifiers—0
A comparison of latent semantic analysis and correspondence analysis of document-term matrices—0
A Composite Kernel Approach for Dialog Topic Tracking with Structured Domain Knowledge from Wikipedia—0
Active Learning with Rationales for Text Classification—0
A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing—0
A good space: Lexical predictors in word space evaluation—0
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