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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 31–40 of 247 papers

TitleStatusHype
NatCat: Weakly Supervised Text Classification with Naturally Annotated ResourcesCode1
Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based ApproachCode0
Deep learning models for representing out-of-vocabulary words—0
Sequence-to-Set Semantic Tagging for Complex Query Reformulation and Automated Text Categorization in Biomedical IR using Self-Attention—0
Improve Document Embedding for Text Categorization Through Deep Siamese Neural Network—0
Text Categorization for Conflict Event Annotation—0
An approach based on Combination of Features for automatic news retrieval—0
SeMemNN: A Semantic Matrix-Based Memory Neural Network for Text ClassificationCode0
A Machine Learning Framework for Authorship Identification From Texts—0
PySS3: A Python package implementing a novel text classifier with visualization tools for Explainable AICode0
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