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Text Categorization
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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
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Effects of term weighting approach with and without stop words removing on Arabic text classification
Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders
Text Categorization Can Enhance Domain-Agnostic Stopword Extraction
Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive Study
Beyond original Research Articles Categorization via NLP
Consistent Text Categorization using Data Augmentation in e-Commerce
Tuning Traditional Language Processing Approaches for Pashto Text Classification
SA-CNN: Application to text categorization issues using simulated annealing-based convolutional neural network optimization
Quantum Recurrent Neural Networks for Sequential Learning
Very Large Language Model as a Unified Methodology of Text Mining
Improving Pre-Trained Weights Through Meta-Heuristics Fine-Tuning
High-performance automatic categorization and attribution of inventory catalogs
Monitoring Energy Trends through Automatic Information Extraction
Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization
Using Word Embeddings for Italian Crime News Categorization
A Proposal of Automatic Error Correction in Text
Text Ranking and Classification using Data Compression
A comparison of latent semantic analysis and correspondence analysis of document-term matrices
BERT-based Chinese Text Classification for Emergency Domain with a Novel Loss Function
Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set
NatCat: Weakly Supervised Text Classification with Naturally Annotated Resources
Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
Deep learning models for representing out-of-vocabulary words
Improve Document Embedding for Text Categorization Through Deep Siamese Neural Network
An approach based on Combination of Features for automatic news retrieval
SeMemNN: A Semantic Matrix-Based Memory Neural Network for Text Classification
A Machine Learning Framework for Authorship Identification From Texts
PySS3: A Python package implementing a novel text classifier with visualization tools for Explainable AI
A Hidden Variables Approach to Multilabel Logistic Regression
A Comparative Study of Pretrained Language Models on Thai Social Text Categorization
Improving Document Classification with Multi-Sense Embeddings
Sequence-to-Set Semantic Tagging: End-to-End Multi-label Prediction using Neural Attention for Complex Query Reformulation and Automated Text Categorization
t-SS3: a text classifier with dynamic n-grams for early risk detection over text streams
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
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