Multi-Label Text Classification
71 papers with code • 20 benchmarks • 13 datasets
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."
Benchmarks
These leaderboards are used to track progress in Multi-Label Text Classification
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Libraries
Use these libraries to find Multi-Label Text Classification models and implementationsDatasets
MIMIC-III
RCV1
Reuters-21578
SV-Ident
Antibody Watch
Most implemented papers
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification
Extreme multi-label text classification (XMTC) is a task for tagging a given text with the most relevant labels from an extremely large label set.
Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation
acadTags/Explainable-Automated-Medical-Coding
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LE initialisation consistently boosted most deep learning models for automated medical coding.
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution
Roche/BalancedLossNLP
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EMNLP 2021
Here, we introduce the application of balancing loss functions for multi-label text classification.
Qwen Technical Report
qwenlm/qwen
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Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
NLTK: The Natural Language Toolkit
napakalas/NLIMED
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NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware.
Regularizing Model Complexity and Label Structure for Multi-Label Text Classification
Multi-label text classifiers need to be carefully regularized to prevent the severe over-fitting in the high dimensional space, and also need to take into account label dependencies in order to make accurate predictions under uncertainty.
An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using MIMIC-III Clinical Notes
lsy3/clinical-notes-diagnosis-dl-nlp
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Conclusion: A set of standard metrics was utilized in assessing the performance of ICD-9 code assignment on MIMIC-III dataset.
RMDL: Random Multimodel Deep Learning for Classification
kk7nc/RMDL
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This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification.
Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification
lancopku/SU4MLC
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EMNLP 2018
We propose a novel model for multi-label text classification, which is based on sequence-to-sequence learning.
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces
Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III.
