[HTML][HTML] Text classification algorithms: A survey
In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …
and texts that require a deeper understanding of machine learning methods to be able to …
Survey on multi-output learning
The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …
It is an important learning problem for decision-making since making decisions in the real …
An analysis of hierarchical text classification using word embeddings
Efficient distributed numerical word representation models (word embeddings) combined
with modern machine learning algorithms have recently yielded considerable improvement …
with modern machine learning algorithms have recently yielded considerable improvement …
Large-scale multi-label text classification on EU legislation
We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain. We
release a new dataset of 57k legislative documents from EURLEX, annotated with~ 4.3 k …
release a new dataset of 57k legislative documents from EURLEX, annotated with~ 4.3 k …
Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications
The choice of the loss function is critical in extreme multi-label learning where the objective
is to annotate each data point with the most relevant subset of labels from an extremely large …
is to annotate each data point with the most relevant subset of labels from an extremely large …
[HTML][HTML] Few-shot and zero-shot multi-label learning for structured label spaces
A Rios, R Kavuluru - Proceedings of the Conference on Empirical …, 2018 - ncbi.nlm.nih.gov
Large multi-label datasets contain labels that occur thousands of times (frequent group),
those that occur only a few times (few-shot group), and labels that never appear in the …
those that occur only a few times (few-shot group), and labels that never appear in the …
Taming pretrained transformers for extreme multi-label text classification
We consider the extreme multi-label text classification (XMC) problem: given an input text,
return the most relevant labels from a large label collection. For example, the input text could …
return the most relevant labels from a large label collection. For example, the input text could …
Dismec: Distributed sparse machines for extreme multi-label classification
R Babbar, B Schölkopf - Proceedings of the tenth ACM international …, 2017 - dl.acm.org
Extreme multi-label classification refers to supervised multi-label learning involving
hundreds of thousands or even millions of labels. Datasets in extreme classification exhibit …
hundreds of thousands or even millions of labels. Datasets in extreme classification exhibit …
An empirical study on large-scale multi-label text classification including few and zero-shot labels
Large-scale Multi-label Text Classification (LMTC) has a wide range of Natural Language
Processing (NLP) applications and presents interesting challenges. First, not all labels are …
Processing (NLP) applications and presents interesting challenges. First, not all labels are …
Bonsai: diverse and shallow trees for extreme multi-label classification
Extreme multi-label classification (XMC) refers to supervised multi-label learning involving
hundreds of thousands or even millions of labels. In this paper, we develop a suite of …
hundreds of thousands or even millions of labels. In this paper, we develop a suite of …