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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 41–50 of 247 papers

TitleStatusHype
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
A Hidden Variables Approach to Multilabel Logistic Regression—0
A Comparative Study of Pretrained Language Models on Thai Social Text Categorization—0
Sequence-to-Set Semantic Tagging: End-to-End Multi-label Prediction using Neural Attention for Complex Query Reformulation and Automated Text Categorization—0
t-SS3: a text classifier with dynamic n-grams for early risk detection over text streamsCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
IFlyLegal: A Chinese Legal System for Consultation, Law Searching, and Document Analysis—0
Ensemble Quantile ClassifierCode0
Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers—0
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