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
| 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
Antibody Watch
Most implemented papers
Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation
The Vector of Locally-Aggregated Word Embeddings (VLAWE) representation of a document is then computed by accumulating the differences between each codeword vector and each word vector (from the document) associated to the respective codeword.
PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model
In this work we focus on fine-tuning a pre-trained BERT model and applying it to patent classification.
Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification
HX-idiot/Hybrid_Attention_XML
•
•
Extreme multi-label text classification (XMTC) aims at tagging a document with most relevant labels from an extremely large-scale label set.
Large-Scale Multi-Label Text Classification on EU Legislation
iliaschalkidis/lmtc-eurlex57k
•
•
ACL 2019
We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain.
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification.
Hierarchical Multi-label Classification of Text with Capsule Networks
Capsule networks have been shown to demonstrate good performance on structured data in the area of visual inference.
Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter
Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen.
Label-Specific Document Representation for Multi-Label Text Classification
EMNLP2019LSAN/LSAN
•
•
IJCNLP 2019
Multi-label text classification (MLTC) aims to tag most relevant labels for the given document.
t-SS3: a text classifier with dynamic n-grams for early risk detection over text streams
SS3 was created to deal with ERD problems naturally since: it supports incremental training and classification over text streams, and it can visually explain its rationale.
PySS3: A Python package implementing a novel text classifier with visualization tools for Explainable AI
A recently introduced text classifier, called SS3, has obtained state-of-the-art performance on the CLEF's eRisk tasks.
