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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 91100 of 247 papers

TitleStatusHype
EEF: Exponentially Embedded Families with Class-Specific Features for Classification0
Effect of small sample size on text categorization with support vector machines0
Effects of term weighting approach with and without stop words removing on Arabic text classification0
Efficient multivariate sequence classification0
Balancing Between Over-Weighting and Under-Weighting in Supervised Term Weighting0
Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization0
Fast and Accurate Decision Trees for Natural Language Processing Tasks0
Fast Sampling for Bayesian Max-Margin Models0
Feature Hashing for Language and Dialect Identification0
Analysis of opinionated text for opinion mining0
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