Multi-Label Text Classification
72 papers with code • 20 benchmarks • 15 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
New York Times Annotated Corpus
Reuters-21578
Latest papers
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.
Automated ICD Coding using Extreme Multi-label Long Text Transformer-based Models
leiboliu/xr-lat
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XR-Transformer, the new SOTA model in the general extreme multi-label text classification domain, and XR-LAT, a novel adaptation of the XR-Transformer model, were also trained on the MIMIC-III dataset.
Hierarchical Multi-Label Classification of Scientific Documents
msadat3/scihtc
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For example, a paper can be assigned to several topics in a hierarchy tree.
OTSeq2Set: An Optimal Transport Enhanced Sequence-to-Set Model for Extreme Multi-label Text Classification
However, such models can't predict a relatively complete and variable-length label subset for each document, because they select positive labels relevant to the document by a fixed threshold or take top k labels in descending order of scores.
Correlation Networks for Extreme Multi-label Text Classification
XunGuangxu/CorNet
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Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2022
This paper develops the Correlation Networks (CorNet) architecture for the extreme multi-label text classification (XMTC) task, where the objective is to tag an input text sequence with the most relevant subset of labels from an extremely large label set.
Exploiting Global and Local Hierarchies for Hierarchical Text Classification
kongds/hbgl
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Existing methods encode label hierarchy in a global view, where label hierarchy is treated as the static hierarchical structure containing all labels.
Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification
Large-scale multi-label text classification (LMTC) aims to associate a document with its relevant labels from a large candidate set.
Multi-relation Message Passing for Multi-label Text Classification
These examples motivate the modelling of multiple types of bi-directional relationships between labels.
GUDN: A novel guide network with label reinforcement strategy for extreme multi-label text classification
wq2581/gudn
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Large-scale pre-trained models have brought a new trend to this problem.
Predicting Job Titles from Job Descriptions with Multi-label Text Classification
Finding a suitable job and hunting for eligible candidates are important to job seeking and human resource agencies.
