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
Dataset of Propaganda Techniques of the State-Sponsored Information Operation of the People's Republic of China
Latest papers
TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
We identify a critical bias in contemporary CLIP-based models, which we denote as \textit{single tag bias}.
Compositional Generalization for Multi-label Text Classification: A Data-Augmentation Approach
yychai74/ld-vae
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Our experiments show that this data augmentation approach significantly improves the compositional generalization capabilities of classification models on our benchmarks, with both generation models surpassing other text generation baselines.
DKEC: Domain Knowledge Enhanced Multi-Label Classification for Electronic Health Records
Multi-label text classification (MLTC) tasks in the medical domain often face long-tail label distribution, where rare classes have fewer training samples than frequent classes.
Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification
simonucl/HJCL
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Hierarchical multi-label text classification (HMTC) aims at utilizing a label hierarchy in multi-label classification.
Qwen Technical Report
QwenLM/Qwen-7B
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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.
Prompt Tuned Embedding Classification for Multi-Label Industry Sector Allocation
eqtpartners/ptec
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All limitations (a), (b), and (c) are addressed by replacing the PLM's language head with a classification head, which is referred to as Prompt Tuned Embedding Classification (PTEC).
MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification
We then extract the dense text representations from the fine-tuned Transformer.
Mao-Zedong At SemEval-2023 Task 4: Label Represention Multi-Head Attention Model With Contrastive Learning-Enhanced Nearest Neighbor Mechanism For Multi-Label Text Classification
peterlau0626/semeval2023-task4-humanvalue
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The study of human values is essential in both practical and theoretical domains.
An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text
coastalcph/multi-label-classification-t5
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Standard methods for multi-label text classification largely rely on encoder-only pre-trained language models, whereas encoder-decoder models have proven more effective in other classification tasks.
The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study
Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature.
