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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 21–30 of 247 papers

TitleStatusHype
TPT: An Empirical Term Selection for Arabic Text Categorization—0
Using Word Embeddings for Italian Crime News Categorization—0
A Proposal of Automatic Error Correction in Text—0
Text Ranking and Classification using Data CompressionCode0
BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network—0
A comparison of latent semantic analysis and correspondence analysis of document-term matrices—0
BERT-based Chinese Text Classification for Emergency Domain with a Novel Loss Function—0
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set—0
Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks—0
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data SetCode0
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