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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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Datasets

Greatest papers with code

Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications

ACL 2019 andyweizhao/capsule

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 (P@1 metric)

CLASSIFICATION MULTI-LABEL TEXT CLASSIFICATION QUESTION ANSWERING

t-SS3: a text classifier with dynamic n-grams for early risk detection over text streams

11 Nov 2019sergioburdisso/pyss3

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.

ANOREXIA DETECTION CLASSIFICATION DOCUMENT CLASSIFICATION MULTI-LABEL TEXT CLASSIFICATION SENTENCE CLASSIFICATION TEXT CATEGORIZATION

Regularizing Model Complexity and Label Structure for Multi-Label Text Classification

1 May 2017cheng-li/pyramid

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.

CLASSIFICATION MULTI-LABEL TEXT CLASSIFICATION

ML-Net: multi-label classification of biomedical texts with deep neural networks

13 Nov 2018ncbi-nlp/BLUE_Benchmark

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.

CLASSIFICATION 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.

CLASSIFICATION MULTI-LABEL TEXT CLASSIFICATION

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".

CLASSIFICATION MULTI-LABEL TEXT CLASSIFICATION NEWS ANNOTATION PRODUCT CATEGORIZATION WEB PAGE TAGGING

Taming Pretrained Transformers for Extreme Multi-label Text Classification

7 May 2019OctoberChang/X-Transformer

However, naively applying deep transformer models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue.

CLASSIFICATION EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL TEXT CLASSIFICATION PRODUCT CATEGORIZATION SENTENCE CLASSIFICATION