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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
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 with no code
Empowering Interdisciplinary Research with BERT-Based Models: An Approach Through SciBERT-CNN with Topic Modeling
Researchers must stay current in their fields by regularly reviewing academic literature, a task complicated by the daily publication of thousands of papers.
Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification
In this paper, we conduct an in-depth study of supervised contrastive learning and its influence on representation in MLTC context.
KeNet:Knowledge-enhanced Doc-Label Attention Network for Multi-label text classification
It is imperative to additionally acknowledge that the significance of knowledge is substantiated in the realm of MLTC.
HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification
The best-performing models aim to learn a static representation by combining document and hierarchical label information.
Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification
Within the scope of natural language processing, the domain of multi-label text classification is uniquely challenging due to its expansive and uneven label distribution.
Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labels
We present experimental results using the original and the proposed efficient LP-ICP on two English and one Czech language data-sets.
Multi-label Text Classification using GloVe and Neural Network Models
Given that the GloVe model requires no further training, the neural network model can be trained more efficiently.
Text2Topic: Multi-Label Text Classification System for Efficient Topic Detection in User Generated Content with Zero-Shot Capabilities
We propose Text to Topic (Text2Topic), which achieves high multi-label classification performance by employing a Bi-Encoder Transformer architecture that utilizes concatenation, subtraction, and multiplication of embeddings on both text and topic.
Accurate Use of Label Dependency in Multi-Label Text Classification Through the Lens of Causality
In this study, we attribute the bias to the model's misuse of label dependency, i. e., the model tends to utilize the correlation shortcut in label dependency rather than fusing text information and label dependency for prediction.
Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification
Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains.
