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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 231–240 of 247 papers

TitleStatusHype
Towards Building a Multilingual Semantic Network: Identifying Interlingual Links in Wikipedia—0
Baselines and Bigrams: Simple, Good Sentiment and Topic Classification—0
Modeling Topic Dependencies in Hierarchical Text Categorization—0
langid.py: An Off-the-shelf Language Identification Tool—0
State-of-the-Art Kernels for Natural Language Processing—0
Effect of small sample size on text categorization with support vector machines—0
Irregularity Detection in Categorized Document Corpora—0
Improving K-Nearest Neighbor Efficacy for Farsi Text Classification—0
SemSim: Resources for Normalized Semantic Similarity Computation Using Lexical Networks—0
A good space: Lexical predictors in word space evaluation—0
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