Multi-Label Text Classification

According to Wikipedia "In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to."

Multi-Label Text Classification

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications

Content

Investigating Capsule Networks with Dynamic Routing for Text Classification

ML-Net: multi-label classification of biomedical texts with deep neural networks

AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification

MIMIC-III, a freely accessible critical care database

Correlation Networks for Extreme Multi-label Text Classification

Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities

Taming Pretrained Transformers for Extreme Multi-label Text Classification

Multi-Label Text Classification using Attention-based Graph Neural Network