Multi-Label Text Classification
72 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
Latest papers with no code
Recent Advances in Hierarchical Multi-label Text Classification: A Survey
Hierarchical multi-label text classification aims to classify the input text into multiple labels, among which the labels are structured and hierarchical.
DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce
Defect Triage is a time-sensitive and critical process in a large-scale agile software development lifecycle for e-commerce.
Imbalanced Multi-label Classification for Business-related Text with Moderately Large Label Spaces
In this study, we compared the performance of four different methods for multi label text classification using a specific imbalanced business dataset.
Retrieval-augmented Multi-label Text Classification
Multi-label text classification (MLC) is a challenging task in settings of large label sets, where label support follows a Zipfian distribution.
F-PABEE: Flexible-patience-based Early Exiting for Single-label and Multi-label text Classification Tasks
Computational complexity and overthinking problems have become the bottlenecks for pre-training language models (PLMs) with millions or even trillions of parameters.
What Do Patients Say About Their Disease Symptoms? Deep Multilabel Text Classification With Human-in-the-Loop Curation for Automatic Labeling of Patient Self Reports of Problems
We develop a rules based linguistic-dictionary using NLP techniques and graph database-based expert phrase-query system to scale the annotation to the remaining cohort generating the machine annotated dataset, and finally build a Keras-Tensorflow based MLTC model for both datasets.
Label Dependencies-aware Set Prediction Networks for Multi-label Text Classification
Multi-label text classification involves extracting all relevant labels from a sentence.
Adopting the Multi-answer Questioning Task with an Auxiliary Metric for Extreme Multi-label Text Classification Utilizing the Label Hierarchy
This study adopts the proposed method and the evaluation metric to the legal domain.
CinPatent: Datasets for Patent Classification
We release the two new datasets with the code of the baselines.
Harnessing label semantics to extract higher performance under noisy label for Company to Industry matching
While this is an exciting prospect, the challenges appear from the need of historical patterns of such tag assignments (or Labeling).
