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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 231240 of 247 papers

TitleStatusHype
Graph-based Semi-Supervised Learning Algorithms for NLP0
langid.py: An Off-the-shelf Language Identification Tool0
Modeling Topic Dependencies in Hierarchical Text Categorization0
Baselines and Bigrams: Simple, Good Sentiment and Topic Classification0
Effect of small sample size on text categorization with support vector machines0
Improving K-Nearest Neighbor Efficacy for Farsi Text Classification0
A good space: Lexical predictors in word space evaluation0
French and German Corpora for Audience-based Text Type Classification0
Is it Useful to Support Users with Lexical Resources? A User Study.0
Irregularity Detection in Categorized Document Corpora0
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