Multi-Label Text Classification Edit Task
Methodology • Text Classification
About
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."
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Greatest papers with code
RMDL: Random Multimodel Deep Learning for Classification
• kk7nc/RMDL
• 
This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification.
Ranked #1 on
on
DOCUMENT CLASSIFICATION FACE RECOGNITION HIERARCHICAL TEXT CLASSIFICATION OF BLURBS (GERMEVAL 2019) IMAGE CLASSIFICATION MULTI-LABEL TEXT CLASSIFICATION UNSUPERVISED PRE-TRAINING
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
ACL 2019
• andyweizhao/capsule_text_classification
•
Obstacles hindering the development of capsule networks for challenging NLP applications include poor scalability to large output spaces and less reliable routing processes.
Ranked #1 on
Text Classification
on RCV1
([email protected] metric)
Investigating Capsule Networks with Dynamic Routing for Text Classification
EMNLP 2018
• andyweizhao/capsule_text_classification
•
In this study, we explore capsule networks with dynamic routing for text classification.
Ranked #4 on
Sentiment Analysis
on MR
MULTI-LABEL TEXT CLASSIFICATION SENTIMENT ANALYSIS SUBJECTIVITY ANALYSIS
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.
DOCUMENT CLASSIFICATION MULTI-LABEL TEXT CLASSIFICATION SENTENCE CLASSIFICATION TEXT CATEGORIZATION
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.
Ranked #1 on
Depression Detection
on eRisk 2017
ANOREXIA DETECTION DOCUMENT CLASSIFICATION MULTI-LABEL TEXT CLASSIFICATION SENTENCE CLASSIFICATION TEXT CATEGORIZATION
Regularizing Model Complexity and Label Structure for Multi-Label Text Classification
Multi-label text classifiers need to be carefully regularized to prevent the severe over-fitting in the high dimensional space, and also need to take into account label dependencies in order to make accurate predictions under uncertainty.
ML-Net: multi-label classification of biomedical texts with deep neural networks
Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the binary or multi-class text classification problems.
FEATURE ENGINEERING MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION OF BIOMEDICAL TEXTS MULTI-LABEL TEXT CLASSIFICATION
Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification
EMNLP 2018
• lancopku/SU4MLC
• 
We propose a novel model for multi-label text classification, which is based on sequence-to-sequence learning.
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
NeurIPS 2019
• yourh/AttentionXML
•
We propose a new label tree-based deep learning model for XMTC, called AttentionXML, with two unique features: 1) a multi-label attention mechanism with raw text as input, which allows to capture the most relevant part of text to each label; and 2) a shallow and wide probabilistic label tree (PLT), which allows to handle millions of labels, especially for "tail labels".
MULTI-LABEL TEXT CLASSIFICATION NEWS ANNOTATION PRODUCT CATEGORIZATION WEB PAGE TAGGING
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.