Extreme Multi-Label Classification
29 papers with code • 0 benchmarks • 2 datasets
Extreme Multi-Label Classification is a supervised learning problem where an instance may be associated with multiple labels. The two main problems are the unbalanced labels in the dataset and the amount of different labels.
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LSHTC
EXTREME CLASSIFICATION
Latest papers with no code
Extreme Multi-Label Skill Extraction Training using Large Language Models
Online job ads serve as a valuable source of information for skill requirements, playing a crucial role in labor market analysis and e-recruitment processes.
Extreme Classification for Answer Type Prediction in Question Answering
In this paper, we propose use of extreme multi-label classification using Transformer models (XBERT) by clustering KG types using structural and semantic features based on question text.
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.
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
We thus propose CascadeXML, an end-to-end multi-resolution learning pipeline, which can harness the multi-layered architecture of a transformer model for attending to different label resolutions with separate feature representations.
Uncertainty in Extreme Multi-label Classification
Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making.
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification
The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC).
Open Vocabulary Extreme Classification Using Generative Models
To develop systems that simplify this process, we introduce the task of open vocabulary XMC (OXMC): given a piece of content, predict a set of labels, some of which may be outside of the known tag set.
On Riemannian Approach for Constrained Optimization Model in Extreme Classification Problems
We propose a novel Riemannian method for solving the Extreme multi-label classification problem that exploits the geometric structure of the sparse low-dimensional local embedding models.
TailMix: Overcoming the Label Sparsity for Extreme Multi-label Classification
Extreme multi-label classification (XMC) aims at finding the most relevant labels from a huge label set at the industrial scale.
Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization
We want to start in a region of weight space a) with low loss value, b) that is favourable for second-order optimization, and c) where the conjugate-gradient (CG) calculations can be performed quickly.
