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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 181190 of 247 papers

TitleStatusHype
The Importance of Calibration for Estimating Proportions from Annotations0
Theoretical Foundations of Forward Feature Selection Methods based on Mutual Information0
These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution0
The Story of the Characters, the DNA and the Native Language0
Topic Models: Accounting Component Structure of Bigrams0
TopicSpam: a Topic-Model based approach for spam detection0
Towards Basque Oral Poetry Analysis: A Machine Learning Approach0
Towards Building a Multilingual Semantic Network: Identifying Interlingual Links in Wikipedia0
Towards Improving Dialogue Topic Tracking Performances with Wikification of Concept Mentions0
Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings0
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