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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 110 of 247 papers

TitleStatusHype
Latent Dirichlet AllocationCode1
Improving Document Classification with Multi-Sense EmbeddingsCode1
NatCat: Weakly Supervised Text Classification with Naturally Annotated ResourcesCode1
Quantum Recurrent Neural Networks for Sequential LearningCode1
A comparison of latent semantic analysis and correspondence analysis of document-term matrices0
A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing0
A Comparative Study of Pretrained Language Models on Thai Social Text Categorization0
A Joint Probabilistic Classification Model of Relevant and Irrelevant Sentences in Mathematical Word Problems0
Active Learning with Rationales for Text Classification0
A Hidden Variables Approach to Multilabel Logistic Regression0
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