Multi Label Text Classification
44 papers with code • 2 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Multi Label Text Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|
Libraries
Use these libraries to find Multi Label Text Classification models and implementations
MNIST
GoEmotions
CARER
HateScore
Most implemented papers
Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter
Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen.
Label-Specific Document Representation for Multi-Label Text Classification
EMNLP2019LSAN/LSAN
•
•
IJCNLP 2019
Multi-label text classification (MLTC) aims to tag most relevant labels for the given document.
Deep Learning Based Multi-Label Text Classification of UNGA Resolutions
Francesco-Sovrano/Deep-Learning-Based-Multi-Label-Text-Classification-of-UNGA-Resolutions
•
•
The main goal of this research is to produce a useful software for United Nations (UN), that could help to speed up the process of qualifying the UN documents following the Sustainable Development Goals (SDGs) in order to monitor the progresses at the world level to fight poverty, discrimination, climate changes.
Label-Wise Document Pre-Training for Multi-Label Text Classification
laddie132/LW-PT
•
•
A major challenge of multi-label text classification (MLTC) is to stimulatingly exploit possible label differences and label correlations.
LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Network
XinyiZ001/LA-HCN
•
•
In this paper, we propose a Label-based Attention for Hierarchical Mutlti-label Text Classification Neural Network (LA-HCN), where the novel label-based attention module is designed to hierarchically extract important information from the text based on the labels from different hierarchy levels.
An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels
iliaschalkidis/lmtc-eurlex57k
•
•
EMNLP 2020
Furthermore, we show that Transformer-based approaches outperform the state-of-the-art in two of the datasets, and we propose a new state-of-the-art method which combines BERT with LWANs.
Large Scale Legal Text Classification Using Transformer Models
LiamMaclean216/Pytorch-Transfomer
•
•
Large multi-label text classification is a challenging Natural Language Processing (NLP) problem that is concerned with text classification for datasets with thousands of labels.
LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification
kongds/LightXML
•
•
In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels.
Does Head Label Help for Long-Tailed Multi-Label Text Classification
To address the challenge of insufficient training data on tail label classification, we propose a Head-to-Tail Network (HTTN) to transfer the meta-knowledge from the data-rich head labels to data-poor tail labels.
MATCH: Metadata-Aware Text Classification in A Large Hierarchy
Multi-label text classification refers to the problem of assigning each given document its most relevant labels from the label set.
