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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

Showing 161170 of 247 papers

TitleStatusHype
Reducing Over-generation Errors for Automatic Keyphrase Extraction using Integer Linear Programming0
Linguistic Template Extraction for Recognizing Reader-Emotion and Emotional Resonance Writing Assistance0
Semantic Clustering and Convolutional Neural Network for Short Text Categorization0
KeLP: a Kernel-based Learning Platform for Natural Language Processing0
Text Categorization as a Graph Classification Problem0
Automatic Keyword Extraction on Twitter0
Radical Embedding: Delving Deeper to Chinese Radicals0
A Method of Accounting Bigrams in Topic Models0
Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning0
SWASH: A Naive Bayes Classifier for Tweet Sentiment Identification0
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